{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "35241771",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score\n",
    "import os\n",
    "\n",
    "# 数据获取\n",
    "train_data = pd.read_csv('data/train_prc.csv')\n",
    "train_tag = pd.read_csv('data/train_tag.csv')\n",
    "train_data = pd.concat([train_data,train_tag],axis=1)\n",
    "\n",
    "val_data = pd.read_csv('data/val_prc.csv')\n",
    "val_tag = pd.read_csv('data/val_tag.csv')\n",
    "val_data = pd.concat([val_data,val_tag],axis=1)\n",
    "val_tag = val_tag.values.ravel()\n",
    "\n",
    "df_norm_test = pd.read_csv('data/test_prc.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "72586dba",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# 特征组合\n",
    "def dataDrop(name,train_data=train_data,val_data=val_data,df_norm_test=df_norm_test):\n",
    "    train_data = train_data.drop(name,axis=1)\n",
    "    val_data = val_data.drop(name,axis=1)\n",
    "    df_norm_test = df_norm_test.drop(name,axis=1)\n",
    "    return train_data,val_data,df_norm_test\n",
    "\n",
    "train_data,val_data,df_norm_test = dataDrop('Name_Class_for_Age',train_data,val_data,df_norm_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "338d0eb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据分组\n",
    "\n",
    "# 将男性女性分开训练\n",
    "def groupCal(train_data,val_data,typeName):\n",
    "    trainGroup = train_data.groupby(typeName,as_index=False)\n",
    "    valGroup = val_data.groupby(typeName,as_index=False)\n",
    "    #trainGroup = df_norm_test.groupby(typeName,as_index=False)\n",
    "    return trainGroup,valGroup \n",
    "trainGroup,valGroup = groupCal(train_data,val_data,'Sex')\n",
    "\n",
    "train_male = trainGroup.get_group(0).reset_index(drop=True)\n",
    "train_female = trainGroup.get_group(1).reset_index(drop=True)\n",
    "val_male = valGroup.get_group(0).reset_index(drop=True)\n",
    "val_female = valGroup.get_group(1).reset_index(drop=True)\n",
    "\n",
    "# 训练前剔除标签\n",
    "train_male_tag = train_male[\"Survived\"]\n",
    "train_male = train_male.drop(\"Survived\",axis=1)\n",
    "train_male = train_male.drop(\"Sex\",axis=1)\n",
    "\n",
    "train_female_tag = train_female[\"Survived\"]\n",
    "train_female = train_female.drop(\"Survived\",axis=1)\n",
    "train_female = train_female.drop(\"Sex\",axis=1)\n",
    "\n",
    "val_male_tag = val_male[\"Survived\"]\n",
    "val_male = val_male.drop(\"Survived\",axis=1)\n",
    "val_male = val_male.drop(\"Sex\",axis=1)\n",
    "val_female_tag = val_female[\"Survived\"]\n",
    "val_female = val_female.drop(\"Survived\",axis=1)\n",
    "val_female = val_female.drop(\"Sex\",axis=1)\n",
    "\n",
    "train_data = train_data.drop(\"Survived\",axis=1)\n",
    "val_data = val_data.drop(\"Survived\",axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "f67c00f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型初始化\n",
    "lg_model = LogisticRegression()\n",
    "svm_model = SVC(kernel='rbf', C=0.5,probability=True)  # C为正则化参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "91e4ff88",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "# 定义需要的特征列表\n",
    "selected_features = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']\n",
    "# 选择特定特征\n",
    "# train_data = train_data[selected_features]\n",
    "# val_data = val_data[selected_features]\n",
    "# test_data  = df_norm_test[selected_features]\n",
    "\n",
    "# 特征组合\n",
    "\n",
    "def trainDataDrop(name,train_data=train_data,val_data=val_data):\n",
    "    train_data = train_data.drop(name,axis=1)\n",
    "    val_data = val_data.drop(name,axis=1)\n",
    "    return train_data,val_data,\n",
    "\n",
    "if(0):\n",
    "    train_data,val_data = trainDataDrop('Survived',train_data,val_data)\n",
    "    train_data,val_data,df_norm_test = dataDrop('Name_Class_for_Age')\n",
    "    # train_data,val_data,df_norm_test = dataDrop('Alone')\n",
    "    # train_data,val_data,df_norm_test = dataDrop('Alone_female')\n",
    "    # train_data,val_data,df_norm_test = dataDrop('Parent_female')\n",
    "if(0):\n",
    "    train_male,val_male = trainDataDrop('Alone',train_male,val_male)\n",
    "    train_male,val_male = trainDataDrop('Alone_female',train_male,val_male)\n",
    "    train_male,val_male = trainDataDrop('Parent_female',train_male,val_male)\n",
    "  \n",
    "    train_female,val_female = trainDataDrop('Alone',train_female,val_female)\n",
    "    train_female,val_female = trainDataDrop('Alone_female',train_female,val_female)\n",
    "    train_female,val_female = trainDataDrop('Parent_female',train_female,val_female)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e6a0d0f",
   "metadata": {},
   "source": [
    "# 方向一 机器学习方案\n",
    "    输出标签为不连续的值，划分为分类问题，由于仅二分类，可用逻辑回归解决。\n",
    "    从轻量级选择线性分类器\n",
    "    尝试使用sklearn.linear_model.LogisticRegression以及SVM\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72f48d54",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "87b93a8f",
   "metadata": {},
   "source": [
    "# 6特征 \n",
    "阈值0.68308     准确率 0.83240 数量149\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "14d046e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/media/py311Venv/kaggle/lib/python3.11/site-packages/sklearn/utils/validation.py:1406: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogReg Accuracy: 0.8232044198895028\n",
      "LogReg_TH Accuracy: 0.8287292817679558,\n",
      " num_correct: 150\n"
     ]
    }
   ],
   "source": [
    "#lg_model = LogisticRegression(penalty='l1', solver='liblinear', C=0.6)\n",
    "\n",
    "lg_model.fit(train_data, train_tag)\n",
    "y_pred_lg = lg_model.predict(val_data)\n",
    "accuracy = accuracy_score(val_tag, y_pred_lg)\n",
    "print(f'LogReg Accuracy: {accuracy}')\n",
    "\n",
    "y_pred_proba_val = lg_model.predict_proba(val_data)\n",
    "y_proba_val = y_pred_proba_val[:, 1]\n",
    "# 设置新的阈值，例如0.3\n",
    "threshold = 0.6#0.68308\n",
    "# 根据新的阈值进行分类：如果概率大于阈值，则预测为类别1，否则为类别0\n",
    "y_pred_new_threshold = (y_proba_val >= threshold).astype(int)\n",
    "accuracy_new = accuracy_score(val_tag, y_pred_new_threshold)\n",
    "#统计下正确数量\n",
    "correct_count = sum(y_pred_new_threshold == val_tag)\n",
    "print(f'LogReg_TH Accuracy: {accuracy_new},\\n num_correct: {correct_count}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "0331dd85",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/media/py311Venv/kaggle/lib/python3.11/site-packages/sklearn/utils/validation.py:1406: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "svm_model Accuracy: 0.8342541436464088\n",
      "svm_model_TH Accuracy: 0.8287292817679558,\n",
      " num_correct: 150\n"
     ]
    }
   ],
   "source": [
    "\n",
    "svm_model.fit(train_data, train_tag)\n",
    "y_pred_svm = svm_model.predict(val_data)\n",
    "accuracy = accuracy_score(val_tag, y_pred_svm)\n",
    "print(f'svm_model Accuracy: {accuracy}')\n",
    "\n",
    "y_pred_proba_val = svm_model.predict_proba(val_data)\n",
    "y_proba_val = y_pred_proba_val[:, 1]\n",
    "# 设置新的阈值，例如0.3\n",
    "threshold = 0.7\n",
    "# 根据新的阈值进行分类：如果概率大于阈值，则预测为类别1，否则为类别0\n",
    "y_pred_new_threshold = (y_proba_val >= threshold).astype(int)\n",
    "accuracy_new = accuracy_score(val_tag, y_pred_new_threshold)\n",
    "correct_count = sum(y_pred_new_threshold == val_tag)\n",
    "print(f'svm_model_TH Accuracy: {accuracy_new},\\n num_correct: {correct_count}')\n",
    "\n",
    "svm_y_pred_proba_val = svm_model.predict_proba(df_norm_test)[:, 1]\n",
    "svm_y_pred = (svm_y_pred_proba_val >= threshold).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "772e0c92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Accuracy: 0.6298342541436464\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/media/py311Venv/kaggle/lib/python3.11/site-packages/sklearn/base.py:1365: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf_model = RandomForestClassifier(\n",
    "    n_estimators=100,      # 决策树的数量\n",
    "    max_depth=100,           # 树的最大深度\n",
    "    min_samples_split=10,  # 分割内部节点所需的最小样本数\n",
    "    min_samples_leaf=5,    # 叶节点上的最小样本数\n",
    "    random_state=42        # 随机种子，确保结果可重现\n",
    ")\n",
    "rf_model.fit(train_data, train_tag)\n",
    "y_pred_rf = rf_model.predict(val_data)\n",
    "accuracy = accuracy_score(val_tag, y_pred_rf)\n",
    "print(f'Random Forest Accuracy: {accuracy}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0f22b42",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "338901a6",
   "metadata": {},
   "source": [
    "\n",
    "# 7特征\n",
    "- max_iter=10000, random_state=42\n",
    "- 0.832   layer (7,10,7)\n",
    "- 0.837   layer (7,10,7)--使用姓名中的Mr信息进行age缺失值的补充\n",
    "- 0.815   layer (14,21,14)\n",
    "- 0.787   layer (7, 14, 14, 7)\n",
    "# 9特征\n",
    "- max_iter=10000, random_state=42\n",
    "- 0.832   layer (9,17,9)\n",
    "- 0.832   layer (9,2)\n",
    "- 0.826  layer (18,27)\n",
    "# 882特征\n",
    " - 0.8547  layer (441,110,2)\n",
    " - 0.8435  layer (1764,441,2) TH 0.4 实际得分0.79905\n",
    "# 886特征\n",
    " - 0.8491  layer (1764,220,2) TH 0.5 实际得分0.8038\n",
    "# 1548特征\n",
    " - 0.8659  layer (3096,220,2) TH 0.5 实际分数0.7703\n",
    " - 0.8826  layer (100,2)      TH 0.3 实际分数0.7751\n",
    " - 0.8491  layer (200,10)     TH 0.9 实际分数0.7635 逐步过拟合\n",
    " # 905特征\n",
    " - 0.8603 layer (200,2)       TH 0.2 实际分数0.7703\n",
    " # 672特征\n",
    " - svm 0.839                  TH 0.7 实际分数0.78947"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "913cab89",
   "metadata": {},
   "source": [
    "# 方案二 深度学习方案\n",
    "    - 由于输入为一维数据，使用MLP进行预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "6cbd3671",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/media/py311Venv/kaggle/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1219: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
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       "<style>#sk-container-id-53 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-53 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-53 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-53 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-53 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-53 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-53 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-53 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-53 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-53 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-53 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-53 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-53 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-53 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-53 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-53 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-53 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-53 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-53 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-53 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-53 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-53 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-53 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-53 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-53 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-53 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-53 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-53 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-53 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-53 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-53 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-53 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-53 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-53 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-53 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-53 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-53 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-53 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-53 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-53 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-53 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-53 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-53 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url();\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-53\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(hidden_layer_sizes=(336, 64), max_iter=1500, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-53\" type=\"checkbox\" checked><label for=\"sk-estimator-id-53\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>MLPClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.neural_network.MLPClassifier.html\">?<span>Documentation for MLPClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('hidden_layer_sizes',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">hidden_layer_sizes&nbsp;</td>\n",
       "            <td class=\"value\">(336, ...)</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('activation',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">activation&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;relu&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('solver',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">solver&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;adam&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('alpha',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">alpha&nbsp;</td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('batch_size',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">batch_size&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('learning_rate',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">learning_rate&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;constant&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('learning_rate_init',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">learning_rate_init&nbsp;</td>\n",
       "            <td class=\"value\">0.001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('power_t',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">power_t&nbsp;</td>\n",
       "            <td class=\"value\">0.5</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_iter&nbsp;</td>\n",
       "            <td class=\"value\">1500</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('shuffle',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">shuffle&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">random_state&nbsp;</td>\n",
       "            <td class=\"value\">42</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">tol&nbsp;</td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">verbose&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('warm_start',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">warm_start&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('momentum',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">momentum&nbsp;</td>\n",
       "            <td class=\"value\">0.9</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('nesterovs_momentum',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">nesterovs_momentum&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('early_stopping',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">early_stopping&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('validation_fraction',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">validation_fraction&nbsp;</td>\n",
       "            <td class=\"value\">0.1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('beta_1',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">beta_1&nbsp;</td>\n",
       "            <td class=\"value\">0.9</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('beta_2',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">beta_2&nbsp;</td>\n",
       "            <td class=\"value\">0.999</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('epsilon',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">epsilon&nbsp;</td>\n",
       "            <td class=\"value\">1e-08</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_iter_no_change',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_iter_no_change&nbsp;</td>\n",
       "            <td class=\"value\">10</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_fun',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_fun&nbsp;</td>\n",
       "            <td class=\"value\">15000</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "</script></body>"
      ],
      "text/plain": [
       "MLPClassifier(hidden_layer_sizes=(336, 64), max_iter=1500, random_state=42)"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "feature_len = train_data.shape[1]\n",
    "#mlp_model = MLPClassifier(hidden_layer_sizes=(feature_len,feature_len*2-2,feature_len),max_iter=10000, random_state=42)# 7,10,7-500-42\n",
    "#mlp_model = MLPClassifier(hidden_layer_sizes=(7,10,7),max_iter=1000, random_state=42)# 7,10,7-500-42\n",
    "#mlp_model = MLPClassifier(hidden_layer_sizes=(1764,220,2),max_iter=1500, random_state=42)# 441,110,2\n",
    "#mlp_model = MLPClassifier(hidden_layer_sizes=(1776,220,2),max_iter=1500, random_state=42)# 441,110,2\n",
    "mlp_model = MLPClassifier(hidden_layer_sizes=(336,64),max_iter=1500, random_state=42)# 441,110,2\n",
    "#mlp_model = MLPClassifier(hidden_layer_sizes=(336,64),max_iter=4000, learning_rate_init=1e-3,tol=1e-5,n_iter_no_change=100,random_state=42,verbose=True,solver='adam')# 441,110,2\n",
    "mlp_model.fit(train_data, train_tag)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ea18dfaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "male 类别分布: {np.float64(0.6216216216216216): np.int64(370), np.float64(2.5555555555555554): np.int64(90)}\n",
      "male 对应权重: {np.float64(0.6216216216216216): np.float64(1.0), np.float64(2.5555555555555554): np.float64(1.0)}\n",
      "female 类别分布: {np.float64(0.6756756756756757): np.int64(185), np.float64(1.9230769230769231): np.int64(65)}\n",
      "female 对应权重: {np.float64(0.6756756756756757): np.float64(1.0), np.float64(1.9230769230769231): np.float64(1.0)}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.utils.class_weight import compute_sample_weight\n",
    "male_sample_weights = compute_sample_weight('balanced', train_male_tag)\n",
    "female_sample_weights = compute_sample_weight('balanced', train_female_tag)\n",
    "\n",
    "unique, counts = np.unique(male_sample_weights, return_counts=True)\n",
    "print(\"male 类别分布:\", dict(zip(unique, counts)))\n",
    "print(\"male 对应权重:\", dict(zip(unique, compute_sample_weight('balanced', unique))))\n",
    "\n",
    "unique, counts = np.unique(female_sample_weights, return_counts=True)\n",
    "print(\"female 类别分布:\", dict(zip(unique, counts)))\n",
    "print(\"female 对应权重:\", dict(zip(unique, compute_sample_weight('balanced', unique))))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "135496bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.61654132\n",
      "Iteration 2, loss = 0.53332208\n",
      "Iteration 3, loss = 0.48201413\n",
      "Iteration 4, loss = 0.46061373\n",
      "Iteration 5, loss = 0.45430017\n",
      "Iteration 6, loss = 0.44870381\n",
      "Iteration 7, loss = 0.43890470\n",
      "Iteration 8, loss = 0.42518971\n",
      "Iteration 9, loss = 0.41245640\n",
      "Iteration 10, loss = 0.39689735\n",
      "Iteration 11, loss = 0.38323940\n",
      "Iteration 12, loss = 0.36855149\n",
      "Iteration 13, loss = 0.35218044\n",
      "Iteration 14, loss = 0.33494500\n",
      "Iteration 15, loss = 0.31608096\n",
      "Iteration 16, loss = 0.29800650\n",
      "Iteration 17, loss = 0.27854761\n",
      "Iteration 18, loss = 0.26127337\n",
      "Iteration 19, loss = 0.24367016\n",
      "Iteration 20, loss = 0.22672187\n",
      "Iteration 21, loss = 0.21198488\n",
      "Iteration 22, loss = 0.19830048\n",
      "Iteration 23, loss = 0.18639905\n",
      "Iteration 24, loss = 0.17584875\n",
      "Iteration 25, loss = 0.16645141\n",
      "Iteration 26, loss = 0.16028213\n",
      "Iteration 27, loss = 0.15327103\n",
      "Iteration 28, loss = 0.14955654\n",
      "Iteration 29, loss = 0.14541992\n",
      "Iteration 30, loss = 0.14013065\n",
      "Iteration 31, loss = 0.13809860\n",
      "Iteration 32, loss = 0.13625989\n",
      "Iteration 33, loss = 0.13294173\n",
      "Iteration 34, loss = 0.13305585\n",
      "Iteration 35, loss = 0.13088734\n",
      "Iteration 36, loss = 0.13054516\n",
      "Iteration 37, loss = 0.13112372\n",
      "Iteration 38, loss = 0.12725088\n",
      "Iteration 39, loss = 0.12613615\n",
      "Iteration 40, loss = 0.12864762\n",
      "Iteration 41, loss = 0.12907738\n",
      "Iteration 42, loss = 0.12399591\n",
      "Iteration 43, loss = 0.12393665\n",
      "Iteration 44, loss = 0.12374238\n",
      "Iteration 45, loss = 0.12370065\n",
      "Iteration 46, loss = 0.12275203\n",
      "Iteration 47, loss = 0.12370868\n",
      "Iteration 48, loss = 0.12397143\n",
      "Iteration 49, loss = 0.12337084\n",
      "Iteration 50, loss = 0.12278297\n",
      "Iteration 51, loss = 0.12174539\n",
      "Iteration 52, loss = 0.12209332\n",
      "Iteration 53, loss = 0.12174070\n",
      "Iteration 54, loss = 0.12084961\n",
      "Iteration 55, loss = 0.12102822\n",
      "Iteration 56, loss = 0.12223266\n",
      "Iteration 57, loss = 0.12138234\n",
      "Iteration 58, loss = 0.12019875\n",
      "Iteration 59, loss = 0.12096308\n",
      "Iteration 60, loss = 0.12051879\n",
      "Iteration 61, loss = 0.11932804\n",
      "Iteration 62, loss = 0.11912604\n",
      "Iteration 63, loss = 0.11974401\n",
      "Iteration 64, loss = 0.11837730\n",
      "Iteration 65, loss = 0.11826777\n",
      "Iteration 66, loss = 0.11872998\n",
      "Iteration 67, loss = 0.11862989\n",
      "Iteration 68, loss = 0.11864795\n",
      "Iteration 69, loss = 0.11792820\n",
      "Iteration 70, loss = 0.11770159\n",
      "Iteration 71, loss = 0.11911271\n",
      "Iteration 72, loss = 0.11823484\n",
      "Iteration 73, loss = 0.11760329\n",
      "Iteration 74, loss = 0.11783769\n",
      "Iteration 75, loss = 0.11701788\n",
      "Iteration 76, loss = 0.11743604\n",
      "Iteration 77, loss = 0.11691725\n",
      "Iteration 78, loss = 0.11707835\n",
      "Iteration 79, loss = 0.11808259\n",
      "Iteration 80, loss = 0.11637529\n",
      "Iteration 81, loss = 0.11843273\n",
      "Iteration 82, loss = 0.11848033\n",
      "Iteration 83, loss = 0.11841641\n",
      "Iteration 84, loss = 0.11701436\n",
      "Iteration 85, loss = 0.11941510\n",
      "Iteration 86, loss = 0.11798459\n",
      "Iteration 87, loss = 0.12202792\n",
      "Iteration 88, loss = 0.11713500\n",
      "Iteration 89, loss = 0.12085759\n",
      "Iteration 90, loss = 0.12158505\n",
      "Iteration 91, loss = 0.11524462\n",
      "Iteration 92, loss = 0.11762320\n",
      "Iteration 93, loss = 0.11598043\n",
      "Iteration 94, loss = 0.11696110\n",
      "Iteration 95, loss = 0.11609522\n",
      "Iteration 96, loss = 0.11489331\n",
      "Iteration 97, loss = 0.11531845\n",
      "Iteration 98, loss = 0.11631502\n",
      "Iteration 99, loss = 0.11643073\n",
      "Iteration 100, loss = 0.11574541\n",
      "Iteration 101, loss = 0.11509087\n",
      "Iteration 102, loss = 0.11457697\n",
      "Iteration 103, loss = 0.11410156\n",
      "Iteration 104, loss = 0.11336750\n",
      "Iteration 105, loss = 0.11485164\n",
      "Iteration 106, loss = 0.11349026\n",
      "Iteration 107, loss = 0.11557844\n",
      "Iteration 108, loss = 0.11836159\n",
      "Iteration 109, loss = 0.11469342\n",
      "Iteration 110, loss = 0.11557075\n",
      "Iteration 111, loss = 0.11621081\n",
      "Iteration 112, loss = 0.11408754\n",
      "Iteration 113, loss = 0.11169400\n",
      "Iteration 114, loss = 0.11896096\n",
      "Iteration 115, loss = 0.11906673\n",
      "Iteration 116, loss = 0.11271947\n",
      "Iteration 117, loss = 0.11676018\n",
      "Iteration 118, loss = 0.11629493\n",
      "Iteration 119, loss = 0.11323457\n",
      "Iteration 120, loss = 0.11316680\n",
      "Iteration 121, loss = 0.11242011\n",
      "Iteration 122, loss = 0.11325974\n",
      "Iteration 123, loss = 0.11163528\n",
      "Iteration 124, loss = 0.11275227\n",
      "Iteration 125, loss = 0.11249362\n",
      "Iteration 126, loss = 0.11265896\n",
      "Iteration 127, loss = 0.11360389\n",
      "Iteration 128, loss = 0.11270583\n",
      "Iteration 129, loss = 0.11219689\n",
      "Iteration 130, loss = 0.11268630\n",
      "Iteration 131, loss = 0.11395122\n",
      "Iteration 132, loss = 0.11208459\n",
      "Iteration 133, loss = 0.11598672\n",
      "Iteration 134, loss = 0.11506101\n",
      "Iteration 135, loss = 0.11329326\n",
      "Iteration 136, loss = 0.11170189\n",
      "Iteration 137, loss = 0.11197977\n",
      "Iteration 138, loss = 0.11551185\n",
      "Iteration 139, loss = 0.11420961\n",
      "Iteration 140, loss = 0.11072537\n",
      "Iteration 141, loss = 0.11264423\n",
      "Iteration 142, loss = 0.11012907\n",
      "Iteration 143, loss = 0.11532434\n",
      "Iteration 144, loss = 0.11513215\n",
      "Iteration 145, loss = 0.11041491\n",
      "Iteration 146, loss = 0.11483405\n",
      "Iteration 147, loss = 0.11455337\n",
      "Iteration 148, loss = 0.11115759\n",
      "Iteration 149, loss = 0.11288437\n",
      "Iteration 150, loss = 0.11030977\n",
      "Iteration 151, loss = 0.11246647\n",
      "Iteration 152, loss = 0.11234259\n",
      "Iteration 153, loss = 0.11024150\n",
      "Iteration 154, loss = 0.11001217\n",
      "Iteration 155, loss = 0.11061563\n",
      "Iteration 156, loss = 0.11253708\n",
      "Iteration 157, loss = 0.11194489\n",
      "Iteration 158, loss = 0.11069804\n",
      "Iteration 159, loss = 0.11024934\n",
      "Iteration 160, loss = 0.11005840\n",
      "Iteration 161, loss = 0.10977836\n",
      "Iteration 162, loss = 0.11095865\n",
      "Iteration 163, loss = 0.11310636\n",
      "Iteration 164, loss = 0.11126949\n",
      "Iteration 165, loss = 0.10910848\n",
      "Iteration 166, loss = 0.11120371\n",
      "Iteration 167, loss = 0.10986369\n",
      "Iteration 168, loss = 0.10918310\n",
      "Iteration 169, loss = 0.11109691\n",
      "Iteration 170, loss = 0.11024212\n",
      "Iteration 171, loss = 0.10929813\n",
      "Iteration 172, loss = 0.10994521\n",
      "Iteration 173, loss = 0.10858329\n",
      "Iteration 174, loss = 0.10874453\n",
      "Iteration 175, loss = 0.10815053\n",
      "Iteration 176, loss = 0.10826222\n",
      "Iteration 177, loss = 0.10856863\n",
      "Iteration 178, loss = 0.11001531\n",
      "Iteration 179, loss = 0.11000538\n",
      "Iteration 180, loss = 0.10959955\n",
      "Iteration 181, loss = 0.10950540\n",
      "Iteration 182, loss = 0.10823492\n",
      "Iteration 183, loss = 0.10945861\n",
      "Iteration 184, loss = 0.10979511\n",
      "Iteration 185, loss = 0.10788163\n",
      "Iteration 186, loss = 0.10826983\n",
      "Iteration 187, loss = 0.10741796\n",
      "Iteration 188, loss = 0.11579522\n",
      "Iteration 189, loss = 0.11107924\n",
      "Iteration 190, loss = 0.11088936\n",
      "Iteration 191, loss = 0.11179219\n",
      "Iteration 192, loss = 0.11124504\n",
      "Iteration 193, loss = 0.10879256\n",
      "Iteration 194, loss = 0.10910956\n",
      "Iteration 195, loss = 0.10968452\n",
      "Iteration 196, loss = 0.10814182\n",
      "Iteration 197, loss = 0.10975103\n",
      "Iteration 198, loss = 0.11162603\n",
      "Iteration 199, loss = 0.10645025\n",
      "Iteration 200, loss = 0.11144825\n",
      "Iteration 201, loss = 0.11381459\n",
      "Iteration 202, loss = 0.10740384\n",
      "Iteration 203, loss = 0.10713743\n",
      "Iteration 204, loss = 0.10833215\n",
      "Iteration 205, loss = 0.10819064\n",
      "Iteration 206, loss = 0.10708384\n",
      "Iteration 207, loss = 0.10941553\n",
      "Iteration 208, loss = 0.10949636\n",
      "Iteration 209, loss = 0.10702260\n",
      "Iteration 210, loss = 0.10703785\n",
      "Iteration 211, loss = 0.10702281\n",
      "Iteration 212, loss = 0.10774782\n",
      "Iteration 213, loss = 0.10741906\n",
      "Iteration 214, loss = 0.10703564\n",
      "Iteration 215, loss = 0.10710924\n",
      "Iteration 216, loss = 0.10799699\n",
      "Iteration 217, loss = 0.10602027\n",
      "Iteration 218, loss = 0.10616284\n",
      "Iteration 219, loss = 0.10614713\n",
      "Iteration 220, loss = 0.10705481\n",
      "Iteration 221, loss = 0.10754677\n",
      "Iteration 222, loss = 0.10622335\n",
      "Iteration 223, loss = 0.10616384\n",
      "Iteration 224, loss = 0.10571253\n",
      "Iteration 225, loss = 0.10612708\n",
      "Iteration 226, loss = 0.10620765\n",
      "Iteration 227, loss = 0.10723122\n",
      "Iteration 228, loss = 0.10624509\n",
      "Iteration 229, loss = 0.10586894\n",
      "Iteration 230, loss = 0.10624435\n",
      "Iteration 231, loss = 0.10608365\n",
      "Iteration 232, loss = 0.10610171\n",
      "Iteration 233, loss = 0.10622885\n",
      "Iteration 234, loss = 0.10594297\n",
      "Iteration 235, loss = 0.10522534\n",
      "Iteration 236, loss = 0.10787431\n",
      "Iteration 237, loss = 0.10833007\n",
      "Iteration 238, loss = 0.10563770\n",
      "Iteration 239, loss = 0.10561369\n",
      "Iteration 240, loss = 0.10529107\n",
      "Iteration 241, loss = 0.10530733\n",
      "Iteration 242, loss = 0.10516223\n",
      "Iteration 243, loss = 0.10591083\n",
      "Iteration 244, loss = 0.10569048\n",
      "Iteration 245, loss = 0.10733243\n",
      "Iteration 246, loss = 0.10702940\n",
      "Iteration 247, loss = 0.10661707\n",
      "Iteration 248, loss = 0.10502932\n",
      "Iteration 249, loss = 0.10509723\n",
      "Iteration 250, loss = 0.10714450\n",
      "Iteration 251, loss = 0.10482596\n",
      "Iteration 252, loss = 0.10549326\n",
      "Iteration 253, loss = 0.10689193\n",
      "Iteration 254, loss = 0.10578470\n",
      "Iteration 255, loss = 0.10465793\n",
      "Iteration 256, loss = 0.10607840\n",
      "Iteration 257, loss = 0.10724632\n",
      "Iteration 258, loss = 0.10491374\n",
      "Iteration 259, loss = 0.10454159\n",
      "Iteration 260, loss = 0.10456749\n",
      "Iteration 261, loss = 0.10456387\n",
      "Iteration 262, loss = 0.10418867\n",
      "Iteration 263, loss = 0.10447709\n",
      "Iteration 264, loss = 0.10456170\n",
      "Iteration 265, loss = 0.10492972\n",
      "Iteration 266, loss = 0.10402896\n",
      "Iteration 267, loss = 0.10540336\n",
      "Iteration 268, loss = 0.10472856\n",
      "Iteration 269, loss = 0.10536881\n",
      "Iteration 270, loss = 0.10423779\n",
      "Iteration 271, loss = 0.10432122\n",
      "Iteration 272, loss = 0.10555940\n",
      "Iteration 273, loss = 0.10350249\n",
      "Iteration 274, loss = 0.10462944\n",
      "Iteration 275, loss = 0.10523477\n",
      "Iteration 276, loss = 0.10355360\n",
      "Iteration 277, loss = 0.10500858\n",
      "Iteration 278, loss = 0.10518975\n",
      "Iteration 279, loss = 0.10393496\n",
      "Iteration 280, loss = 0.10465432\n",
      "Iteration 281, loss = 0.10349864\n",
      "Iteration 282, loss = 0.10448943\n",
      "Iteration 283, loss = 0.10445147\n",
      "Iteration 284, loss = 0.10372414\n",
      "Iteration 285, loss = 0.10294874\n",
      "Iteration 286, loss = 0.10310649\n",
      "Iteration 287, loss = 0.10418797\n",
      "Iteration 288, loss = 0.10416737\n",
      "Iteration 289, loss = 0.10218080\n",
      "Iteration 290, loss = 0.10446854\n",
      "Iteration 291, loss = 0.10386020\n",
      "Iteration 292, loss = 0.10500300\n",
      "Iteration 293, loss = 0.10781953\n",
      "Iteration 294, loss = 0.10200560\n",
      "Iteration 295, loss = 0.10801478\n",
      "Iteration 296, loss = 0.10671417\n",
      "Iteration 297, loss = 0.10076243\n",
      "Iteration 298, loss = 0.11257671\n",
      "Iteration 299, loss = 0.10470874\n",
      "Iteration 300, loss = 0.10298814\n",
      "Iteration 301, loss = 0.11595728\n",
      "Iteration 302, loss = 0.11055532\n",
      "Iteration 303, loss = 0.10624373\n",
      "Iteration 304, loss = 0.10741737\n",
      "Iteration 305, loss = 0.10328219\n",
      "Iteration 306, loss = 0.10226115\n",
      "Iteration 307, loss = 0.10471920\n",
      "Iteration 308, loss = 0.10312718\n",
      "Iteration 309, loss = 0.10339314\n",
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      "Training loss did not improve more than tol=0.000010 for 100 consecutive epochs. Stopping.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-50 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-50 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-50 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-50 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-50 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-50 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-50 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-50 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-50 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-50 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-50 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-50 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-50 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-50 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-50 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-50 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-50 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-50 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-50 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-50 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-50 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-50 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-50 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-50 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-50 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-50 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-50 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-50 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-50 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-50 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-50 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-50 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-50 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-50 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-50 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-50 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-50 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-50 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-50 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-50 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-50 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-50 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-50 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url();\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-50\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(hidden_layer_sizes=(336, 64), max_iter=4000, n_iter_no_change=100,\n",
       "              random_state=42, tol=1e-05, verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-50\" type=\"checkbox\" checked><label for=\"sk-estimator-id-50\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>MLPClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.neural_network.MLPClassifier.html\">?<span>Documentation for MLPClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('hidden_layer_sizes',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">hidden_layer_sizes&nbsp;</td>\n",
       "            <td class=\"value\">(336, ...)</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('activation',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">activation&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;relu&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('solver',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">solver&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;adam&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('alpha',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">alpha&nbsp;</td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('batch_size',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">batch_size&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('learning_rate',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">learning_rate&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;constant&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('learning_rate_init',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">learning_rate_init&nbsp;</td>\n",
       "            <td class=\"value\">0.001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('power_t',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">power_t&nbsp;</td>\n",
       "            <td class=\"value\">0.5</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_iter&nbsp;</td>\n",
       "            <td class=\"value\">4000</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('shuffle',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">shuffle&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
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       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">random_state&nbsp;</td>\n",
       "            <td class=\"value\">42</td>\n",
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       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">tol&nbsp;</td>\n",
       "            <td class=\"value\">1e-05</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">verbose&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('warm_start',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">warm_start&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('momentum',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">momentum&nbsp;</td>\n",
       "            <td class=\"value\">0.9</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('nesterovs_momentum',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">nesterovs_momentum&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('early_stopping',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">early_stopping&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
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       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('validation_fraction',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">validation_fraction&nbsp;</td>\n",
       "            <td class=\"value\">0.1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('beta_1',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">beta_1&nbsp;</td>\n",
       "            <td class=\"value\">0.9</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('beta_2',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">beta_2&nbsp;</td>\n",
       "            <td class=\"value\">0.999</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('epsilon',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">epsilon&nbsp;</td>\n",
       "            <td class=\"value\">1e-08</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_iter_no_change',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_iter_no_change&nbsp;</td>\n",
       "            <td class=\"value\">100</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_fun',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_fun&nbsp;</td>\n",
       "            <td class=\"value\">15000</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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      ],
      "text/plain": [
       "MLPClassifier(hidden_layer_sizes=(336, 64), max_iter=4000, n_iter_no_change=100,\n",
       "              random_state=42, tol=1e-05, verbose=True)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlp_model_male = MLPClassifier(hidden_layer_sizes=(336,64),max_iter=4000,learning_rate_init=1e-3,tol=1e-5,n_iter_no_change=100,random_state=42,verbose=True,solver='adam')# 441,110,2, solver='lbfgs'\n",
    "mlp_model_male.fit(train_male, train_male_tag)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "d966b1ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.71298180\n",
      "Iteration 2, loss = 0.64309956\n",
      "Iteration 3, loss = 0.59409975\n",
      "Iteration 4, loss = 0.56140198\n",
      "Iteration 5, loss = 0.53532908\n",
      "Iteration 6, loss = 0.51241298\n",
      "Iteration 7, loss = 0.49219439\n",
      "Iteration 8, loss = 0.47136058\n",
      "Iteration 9, loss = 0.45027511\n",
      "Iteration 10, loss = 0.42644181\n",
      "Iteration 11, loss = 0.39986755\n",
      "Iteration 12, loss = 0.37390799\n",
      "Iteration 13, loss = 0.34716189\n",
      "Iteration 14, loss = 0.32235233\n",
      "Iteration 15, loss = 0.29857596\n",
      "Iteration 16, loss = 0.27871841\n",
      "Iteration 17, loss = 0.25827343\n",
      "Iteration 18, loss = 0.23840214\n",
      "Iteration 19, loss = 0.21788200\n",
      "Iteration 20, loss = 0.19985616\n",
      "Iteration 21, loss = 0.18528119\n",
      "Iteration 22, loss = 0.17286064\n",
      "Iteration 23, loss = 0.15994318\n",
      "Iteration 24, loss = 0.14626433\n",
      "Iteration 25, loss = 0.13361300\n",
      "Iteration 26, loss = 0.12394251\n",
      "Iteration 27, loss = 0.11647218\n",
      "Iteration 28, loss = 0.11047538\n",
      "Iteration 29, loss = 0.10206049\n",
      "Iteration 30, loss = 0.09482168\n",
      "Iteration 31, loss = 0.08880866\n",
      "Iteration 32, loss = 0.08534472\n",
      "Iteration 33, loss = 0.08086429\n",
      "Iteration 34, loss = 0.07550115\n",
      "Iteration 35, loss = 0.07297516\n",
      "Iteration 36, loss = 0.07454098\n",
      "Iteration 37, loss = 0.07378712\n",
      "Iteration 38, loss = 0.06847594\n",
      "Iteration 39, loss = 0.06435486\n",
      "Iteration 40, loss = 0.06555705\n",
      "Iteration 41, loss = 0.06536420\n",
      "Iteration 42, loss = 0.06307146\n",
      "Iteration 43, loss = 0.06160675\n",
      "Iteration 44, loss = 0.05780659\n",
      "Iteration 45, loss = 0.05861017\n",
      "Iteration 46, loss = 0.06097679\n",
      "Iteration 47, loss = 0.06064137\n",
      "Iteration 48, loss = 0.05780822\n",
      "Iteration 49, loss = 0.05595308\n",
      "Iteration 50, loss = 0.05495907\n",
      "Iteration 51, loss = 0.05483952\n",
      "Iteration 52, loss = 0.05401247\n",
      "Iteration 53, loss = 0.05510438\n",
      "Iteration 54, loss = 0.05420304\n",
      "Iteration 55, loss = 0.05320284\n",
      "Iteration 56, loss = 0.05260991\n",
      "Iteration 57, loss = 0.05189972\n",
      "Iteration 58, loss = 0.05178156\n",
      "Iteration 59, loss = 0.05090124\n",
      "Iteration 60, loss = 0.05345015\n",
      "Iteration 61, loss = 0.05265133\n",
      "Iteration 62, loss = 0.05162131\n",
      "Iteration 63, loss = 0.05112806\n",
      "Iteration 64, loss = 0.05218256\n",
      "Iteration 65, loss = 0.05085500\n",
      "Iteration 66, loss = 0.05158552\n",
      "Iteration 67, loss = 0.05436251\n",
      "Iteration 68, loss = 0.05570398\n",
      "Iteration 69, loss = 0.05300645\n",
      "Iteration 70, loss = 0.04957368\n",
      "Iteration 71, loss = 0.05175612\n",
      "Iteration 72, loss = 0.05147329\n",
      "Iteration 73, loss = 0.04962220\n",
      "Iteration 74, loss = 0.04955003\n",
      "Iteration 75, loss = 0.04991193\n",
      "Iteration 76, loss = 0.05052259\n",
      "Iteration 77, loss = 0.05052988\n",
      "Iteration 78, loss = 0.05003411\n",
      "Iteration 79, loss = 0.04993144\n",
      "Iteration 80, loss = 0.04954702\n",
      "Iteration 81, loss = 0.05024735\n",
      "Iteration 82, loss = 0.05050634\n",
      "Iteration 83, loss = 0.05026433\n",
      "Iteration 84, loss = 0.04915057\n",
      "Iteration 85, loss = 0.04919053\n",
      "Iteration 86, loss = 0.04882395\n",
      "Iteration 87, loss = 0.04867437\n",
      "Iteration 88, loss = 0.04785966\n",
      "Iteration 89, loss = 0.04844671\n",
      "Iteration 90, loss = 0.04796739\n",
      "Iteration 91, loss = 0.04769181\n",
      "Iteration 92, loss = 0.04775995\n",
      "Iteration 93, loss = 0.04842815\n",
      "Iteration 94, loss = 0.04946876\n",
      "Iteration 95, loss = 0.04868384\n",
      "Iteration 96, loss = 0.04830020\n",
      "Iteration 97, loss = 0.04963727\n",
      "Iteration 98, loss = 0.04917256\n",
      "Iteration 99, loss = 0.04790202\n",
      "Iteration 100, loss = 0.04884222\n",
      "Iteration 101, loss = 0.05309246\n",
      "Iteration 102, loss = 0.05474781\n",
      "Iteration 103, loss = 0.05003562\n",
      "Iteration 104, loss = 0.04663531\n",
      "Iteration 105, loss = 0.04942237\n",
      "Iteration 106, loss = 0.05174657\n",
      "Iteration 107, loss = 0.04947681\n",
      "Iteration 108, loss = 0.04686553\n",
      "Iteration 109, loss = 0.04802619\n",
      "Iteration 110, loss = 0.04752778\n",
      "Iteration 111, loss = 0.04577999\n",
      "Iteration 112, loss = 0.04632550\n",
      "Iteration 113, loss = 0.05263428\n",
      "Iteration 114, loss = 0.05716108\n",
      "Iteration 115, loss = 0.05347309\n",
      "Iteration 116, loss = 0.04803915\n",
      "Iteration 117, loss = 0.04702818\n",
      "Iteration 118, loss = 0.04718542\n",
      "Iteration 119, loss = 0.04659416\n",
      "Iteration 120, loss = 0.04593902\n",
      "Iteration 121, loss = 0.04597235\n",
      "Iteration 122, loss = 0.04670121\n",
      "Iteration 123, loss = 0.04743746\n",
      "Iteration 124, loss = 0.04742598\n",
      "Iteration 125, loss = 0.04650657\n",
      "Iteration 126, loss = 0.04658850\n",
      "Iteration 127, loss = 0.04746919\n",
      "Iteration 128, loss = 0.04583556\n",
      "Iteration 129, loss = 0.04921003\n",
      "Iteration 130, loss = 0.05146628\n",
      "Iteration 131, loss = 0.04821476\n",
      "Iteration 132, loss = 0.04495687\n",
      "Iteration 133, loss = 0.04882974\n",
      "Iteration 134, loss = 0.05196544\n",
      "Iteration 135, loss = 0.05041163\n",
      "Iteration 136, loss = 0.04574229\n",
      "Iteration 137, loss = 0.04721191\n",
      "Iteration 138, loss = 0.04981234\n",
      "Iteration 139, loss = 0.04919024\n",
      "Iteration 140, loss = 0.04791726\n",
      "Iteration 141, loss = 0.04485206\n",
      "Iteration 142, loss = 0.04475835\n",
      "Iteration 143, loss = 0.04551350\n",
      "Iteration 144, loss = 0.04566294\n",
      "Iteration 145, loss = 0.04519782\n",
      "Iteration 146, loss = 0.04551023\n",
      "Iteration 147, loss = 0.04481448\n",
      "Iteration 148, loss = 0.04493305\n",
      "Iteration 149, loss = 0.04460353\n",
      "Iteration 150, loss = 0.04442260\n",
      "Iteration 151, loss = 0.04521577\n",
      "Iteration 152, loss = 0.04821499\n",
      "Iteration 153, loss = 0.04922302\n",
      "Iteration 154, loss = 0.04633871\n",
      "Iteration 155, loss = 0.04373076\n",
      "Iteration 156, loss = 0.04628913\n",
      "Iteration 157, loss = 0.04833288\n",
      "Iteration 158, loss = 0.04662171\n",
      "Iteration 159, loss = 0.04354449\n",
      "Iteration 160, loss = 0.04509482\n",
      "Iteration 161, loss = 0.05266214\n",
      "Iteration 162, loss = 0.05747707\n",
      "Iteration 163, loss = 0.05505741\n",
      "Iteration 164, loss = 0.04775582\n",
      "Iteration 165, loss = 0.04536939\n",
      "Iteration 166, loss = 0.04750568\n",
      "Iteration 167, loss = 0.05161347\n",
      "Iteration 168, loss = 0.04827846\n",
      "Iteration 169, loss = 0.04308078\n",
      "Iteration 170, loss = 0.04689842\n",
      "Iteration 171, loss = 0.05087731\n",
      "Iteration 172, loss = 0.05020105\n",
      "Iteration 173, loss = 0.04759817\n",
      "Iteration 174, loss = 0.04360472\n",
      "Iteration 175, loss = 0.04479400\n",
      "Iteration 176, loss = 0.04636107\n",
      "Iteration 177, loss = 0.04566527\n",
      "Iteration 178, loss = 0.04465062\n",
      "Iteration 179, loss = 0.04598789\n",
      "Iteration 180, loss = 0.04393176\n",
      "Iteration 181, loss = 0.04399953\n",
      "Iteration 182, loss = 0.04471565\n",
      "Iteration 183, loss = 0.04529280\n",
      "Iteration 184, loss = 0.04363077\n",
      "Iteration 185, loss = 0.04365393\n",
      "Iteration 186, loss = 0.04369517\n",
      "Iteration 187, loss = 0.04457208\n",
      "Iteration 188, loss = 0.04440197\n",
      "Iteration 189, loss = 0.04526325\n",
      "Iteration 190, loss = 0.04462579\n",
      "Iteration 191, loss = 0.04389214\n",
      "Iteration 192, loss = 0.04494626\n",
      "Iteration 193, loss = 0.04453783\n",
      "Iteration 194, loss = 0.04369735\n",
      "Iteration 195, loss = 0.04297613\n",
      "Iteration 196, loss = 0.04372027\n",
      "Iteration 197, loss = 0.04366095\n",
      "Iteration 198, loss = 0.04306907\n",
      "Iteration 199, loss = 0.04307901\n",
      "Iteration 200, loss = 0.04401893\n",
      "Iteration 201, loss = 0.04483637\n",
      "Iteration 202, loss = 0.04444913\n",
      "Iteration 203, loss = 0.04330631\n",
      "Iteration 204, loss = 0.04288952\n",
      "Iteration 205, loss = 0.04449700\n",
      "Iteration 206, loss = 0.04384572\n",
      "Iteration 207, loss = 0.04231036\n",
      "Iteration 208, loss = 0.04310176\n",
      "Iteration 209, loss = 0.04319554\n",
      "Iteration 210, loss = 0.04286763\n",
      "Iteration 211, loss = 0.04506185\n",
      "Iteration 212, loss = 0.04486313\n",
      "Iteration 213, loss = 0.04351596\n",
      "Iteration 214, loss = 0.04329197\n",
      "Iteration 215, loss = 0.04330034\n",
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      "Iteration 512, loss = 0.04353494\n",
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      "Iteration 514, loss = 0.03798766\n",
      "Iteration 515, loss = 0.04182864\n",
      "Iteration 516, loss = 0.04583887\n",
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      "Iteration 518, loss = 0.03751497\n",
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      "Iteration 520, loss = 0.04558688\n",
      "Iteration 521, loss = 0.05127661\n",
      "Iteration 522, loss = 0.05006361\n",
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      "Iteration 524, loss = 0.03889045\n",
      "Iteration 525, loss = 0.03937973\n",
      "Iteration 526, loss = 0.04058991\n",
      "Iteration 527, loss = 0.04045802\n",
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      "Iteration 529, loss = 0.03665403\n",
      "Iteration 530, loss = 0.04021302\n",
      "Iteration 531, loss = 0.04397611\n",
      "Training loss did not improve more than tol=0.000010 for 100 consecutive epochs. Stopping.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-52 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-52 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-52 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-52 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-52 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-52 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-52 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-52 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-52 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-52 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-52 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-52 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-52 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-52 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-52 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-52 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-52 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-52 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-52 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-52 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-52 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-52 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-52 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-52 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-52 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-52 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-52 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-52 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-52 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-52 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-52 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-52 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-52 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-52 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-52 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-52 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-52 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-52 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-52 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-52 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-52 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-52 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-52 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url();\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-52\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(hidden_layer_sizes=(336, 64), max_iter=4000, n_iter_no_change=100,\n",
       "              random_state=42, tol=1e-05, verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-52\" type=\"checkbox\" checked><label for=\"sk-estimator-id-52\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>MLPClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.neural_network.MLPClassifier.html\">?<span>Documentation for MLPClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('hidden_layer_sizes',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">hidden_layer_sizes&nbsp;</td>\n",
       "            <td class=\"value\">(336, ...)</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('activation',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">activation&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;relu&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('solver',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">solver&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;adam&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('alpha',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">alpha&nbsp;</td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('batch_size',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">batch_size&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('learning_rate',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">learning_rate&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;constant&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('learning_rate_init',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">learning_rate_init&nbsp;</td>\n",
       "            <td class=\"value\">0.001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('power_t',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">power_t&nbsp;</td>\n",
       "            <td class=\"value\">0.5</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_iter&nbsp;</td>\n",
       "            <td class=\"value\">4000</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('shuffle',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">shuffle&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">random_state&nbsp;</td>\n",
       "            <td class=\"value\">42</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">tol&nbsp;</td>\n",
       "            <td class=\"value\">1e-05</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">verbose&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('warm_start',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">warm_start&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('momentum',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">momentum&nbsp;</td>\n",
       "            <td class=\"value\">0.9</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('nesterovs_momentum',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">nesterovs_momentum&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('early_stopping',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">early_stopping&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('validation_fraction',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">validation_fraction&nbsp;</td>\n",
       "            <td class=\"value\">0.1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('beta_1',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">beta_1&nbsp;</td>\n",
       "            <td class=\"value\">0.9</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('beta_2',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">beta_2&nbsp;</td>\n",
       "            <td class=\"value\">0.999</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('epsilon',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">epsilon&nbsp;</td>\n",
       "            <td class=\"value\">1e-08</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_iter_no_change',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_iter_no_change&nbsp;</td>\n",
       "            <td class=\"value\">100</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_fun',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_fun&nbsp;</td>\n",
       "            <td class=\"value\">15000</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "</script></body>"
      ],
      "text/plain": [
       "MLPClassifier(hidden_layer_sizes=(336, 64), max_iter=4000, n_iter_no_change=100,\n",
       "              random_state=42, tol=1e-05, verbose=True)"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlp_model_female = MLPClassifier(hidden_layer_sizes=(336,64),max_iter=4000,learning_rate_init=1e-3,tol=1e-5,n_iter_no_change=100,random_state=42,verbose=True, solver='adam')# 441,110,2, solver='lbfgs'\n",
    "mlp_model_female.fit(train_female, train_female_tag)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e14fdff9",
   "metadata": {},
   "source": [
    "# 方案三 Xboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "6eeee2ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix, recall_score, roc_auc_score\n",
    "import xgboost as xgb\n",
    "import shap\n",
    "\n",
    "def train_gender_model(X, y,use_pose_weight=0):  \n",
    "\n",
    "    if use_pose_weight:\n",
    "        class1_cnt = len(y[y==0])\n",
    "        class2_cnt = len(y[y==1])\n",
    "        pos_weight = class1_cnt/class2_cnt\n",
    "        \n",
    "        sample_weight = np.where((X['Age'] < 0.4) & (X['SibSp'] == 0) & (X['Parch'] == 0), 20.0,1.0)    \n",
    "        model = xgb.XGBClassifier(\n",
    "        #scale_pos_weight=pos_weight,     # 自动处理不平衡\n",
    "        max_depth=4,                     # 防止过拟合\n",
    "        learning_rate=0.01,\n",
    "        n_estimators=200,\n",
    "        subsample=0.8,\n",
    "        colsample_bytree=0.8,\n",
    "        random_state=42,\n",
    "        eval_metric='logloss'\n",
    "        )\n",
    "        model.fit(X, y,sample_weight = sample_weight)\n",
    "    else:\n",
    "        model = xgb.XGBClassifier(\n",
    "        max_depth=4,                     # 防止过拟合\n",
    "        learning_rate=0.01,\n",
    "        n_estimators=200,\n",
    "        subsample=0.8,\n",
    "        colsample_bytree=0.8,\n",
    "        random_state=42,\n",
    "        eval_metric='logloss'\n",
    "        )\n",
    "        model.fit(X, y)\n",
    "    return model\n",
    "\n",
    "male_xgb = train_gender_model(train_male, train_male_tag,1)\n",
    "female_xgb = train_gender_model(train_female, train_female_tag,1)\n",
    "all_xgb = train_gender_model(train_data, train_tag,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "ba0f8e97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x650 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x650 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x650 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x950 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x950 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import shap\n",
    "\n",
    "explainer = shap.TreeExplainer(male_xgb)\n",
    "shap_explanation = explainer(train_male)\n",
    "\n",
    "# 找出 FN 样本\n",
    "fn_mask = (train_male_tag == 1) & (male_xgb.predict(train_male) == 0)\n",
    "\n",
    "# 可视化前几个 FN 样本的 SHAP 值\n",
    "for i in range(min(3, len(fn_mask))):\n",
    "    idx = np.where(fn_mask)[0][i]\n",
    "    shap.waterfall_plot(\n",
    "        shap_explanation[idx]\n",
    "    )\n",
    "    \n",
    "shap.summary_plot(shap_explanation, train_male, plot_type=\"bar\")\n",
    "\n",
    "# 全局分布图\n",
    "shap.summary_plot(shap_explanation, train_male)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "1edf596f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mlp_modelTH 0.5 Accuracy: 0.8205128205128205\n",
      "mlp_modelTH tp_cnt: 96\n",
      "mlp_modelTH 0.5 Accuracy: 0.859375\n",
      "mlp_modelTH tp_cnt: 55\n",
      "mlp_model tp_data: 0,tn_data: 20,fp_data: 19,fn_data: 2\n",
      "mlp_model tp_data: 20,tn_data: 10,fp_data: 3,fn_data: 6\n",
      "mlp_modelTH 0.5 Accuracy: 0.8342541436464088\n",
      "mlp_modelTH tp_cnt: 151\n",
      "mlp_model tp_data: 20,tn_data: 20,fp_data: 24,fn_data: 3\n"
     ]
    }
   ],
   "source": [
    "# 模型测试与FPTN样本获取\n",
    "\n",
    "model_type = 1 #0-mpl_model 1-xboost_model\n",
    "match model_type:\n",
    "    case 0:\n",
    "        model_male = mlp_model_male\n",
    "        model_female = mlp_model_female\n",
    "        modle_all = mlp_model\n",
    "    case 1:\n",
    "        model_male = male_xgb\n",
    "        model_female = female_xgb\n",
    "        modle_all = all_xgb\n",
    "    case _:\n",
    "        print('model_type error')\n",
    "    \n",
    "def get_FPFN(y_prob,y_tag,threshold,val_data):\n",
    "    y_pred_new_threshold = (y_prob >= threshold).astype(int)\n",
    "    tp_data = []\n",
    "    tn_data = []\n",
    "    fp_data = []\n",
    "    fn_data = []\n",
    "\n",
    "    tp_cnt,tn_cnt,fp_cnt,fn_cnt = 0,0,0,0\n",
    "    for index, value in enumerate(y_prob):  \n",
    "        if y_tag[index] == 1:\n",
    "            if value > 0.5 and tp_cnt < 20:\n",
    "                tp_cnt+=1\n",
    "                tp_data.append(val_data.iloc[index])\n",
    "            elif value < 0.5 and fn_cnt < 20:\n",
    "                fp_cnt+=1\n",
    "                fp_data.append(val_data.iloc[index])\n",
    "        elif y_tag[index] == 0:\n",
    "            if value < 0.5 and tn_cnt < 20:\n",
    "                tn_cnt+=1\n",
    "                tn_data.append(val_data.iloc[index])\n",
    "            elif value > 0.5 and fp_cnt < 20:\n",
    "                fn_cnt+=1\n",
    "                fn_data.append(val_data.iloc[index])\n",
    "\n",
    "    print(f'mlp_model tp_data: {len(tp_data)},tn_data: {len(tn_data)},fp_data: {len(fp_data)},fn_data: {len(fn_data)}')\n",
    "    return tp_data,tn_data,fp_data,fn_data,y_pred_new_threshold\n",
    "\n",
    "def test_model(mlp_model,train_data, val_data, val_tag,threshold = 0.5):\n",
    "    y_pred_proba_val = mlp_model.predict_proba(val_data)\n",
    "    y_pred_proba_class1_val = y_pred_proba_val[:, 1]\n",
    "    # 根据新的阈值进行分类：如果概率大于阈值，则预测为类别1，否则为类别0\n",
    "    y_pred_new_threshold = (y_pred_proba_class1_val >= threshold).astype(int)\n",
    "    accuracy_new = accuracy_score(val_tag, y_pred_new_threshold)\n",
    "    print(f'mlp_modelTH {threshold} Accuracy: {accuracy_new}')\n",
    "    print(f'mlp_modelTH tp_cnt: {sum(y_pred_new_threshold == val_tag)}')\n",
    "    train_prob = mlp_model.predict_proba(train_data)\n",
    "    return y_pred_proba_class1_val,train_prob\n",
    "\n",
    "threshold = 0.5\n",
    "y_prob_c1_male_val,y_prob_c1_male_train = test_model(model_male, train_male,val_male, val_male_tag,threshold)\n",
    "y_prob_c1_female_val,y_prob_c1_female_train = test_model(model_female,train_female, val_female, val_female_tag,threshold)\n",
    "\n",
    "tp_data_male,tn_data_male,fp_data_male,fn_data_male,male_rslt_af_th = get_FPFN(y_prob_c1_male_val,val_male_tag,threshold,val_data=val_male)\n",
    "tp_data_female,tn_data_female,fp_data_female,fn_data_female,female_rslt_af_th = get_FPFN(y_prob_c1_female_val,val_female_tag,threshold,val_data=val_female)\n",
    "\n",
    "\n",
    "y_prob_c1_all_val,y_prob_c1_all_train = test_model(modle_all, train_data,val_data,val_tag,threshold)\n",
    "tp_data_all,tn_data_all,fp_data_all,fn_data_all,all_rslt_af_th = get_FPFN(y_prob_c1_all_val,val_tag,threshold,val_data=val_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "cc01614b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测概率统计信息:\n",
      "  平均概率: 0.1735,概率标准差: 0.0879,最小概率: 0.0831,最大概率: 0.5155\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测概率统计信息:\n",
      "  平均概率: 0.7907,概率标准差: 0.2028,最小概率: 0.3623,最大概率: 0.9569\n"
     ]
    },
    {
     "data": {
      "image/png": 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nCm5ubsI///xjsP/7778vODg4CPfu3dOXARA+/vjjfGP+7LPPBAcHB0GhUAiCIAjLli0TQkJChFdeeUV47733BEEQBI1GI3h5eRU4LsprnK3b9tl/uwVBEHr27Cn4+PjkG58gmD/+s8R3sUePHoKLi4sQFxenL7t69arg4OBQ4Dhw5syZAgBh586dRn3QHTevMVZedO9dt27dDMpHjx4tABD++usvfVle+VyPHj0EZ2dn4fbt2/qyhw8fCh4eHkKLFi30ZbrveaNGjfTvqSAIwsKFCwUAwp49e/Rlpr7XI0eOFFxdXQ3GV+Z+drqx3LPvizlj7x07dpj8m6DRaISKFSsK/fr1MyhftGiRIBKJhDt37hjFnxebueXeHC1btkTNmjWNyqVSqf7/Hz9+jPT0dDRv3hwXLlwwq90RI0YYnOFs3rw5NBoN4uLi8twnPj4eFy9exJAhQ+Dp6akvb9euXYnE+Oz+OTk5SElJQdWqVeHl5WV2Gz169DC4+vjKK6+gSZMm+Pnnn422HTVqlMHrHTt2wNPTE+3atcOjR4/0P40aNYK7u7v+FuPffvtNfwXy2fd04sSJBcb3559/IjY2FhMnTtRfRdUp7hnowMBA9OzZU/9ad3vOn3/+iYSEBINt3377bYMr0ro+TZw40eDZ87fffhsymQz79u0z2N/R0REjR47Uv3Z2dsbIkSORlJSE8+fPAwAcHBzg7OwM4OmtY6mpqcjNzcXLL79s8vMszGdnKREREQgMDMSWLVv0ZZcvX8bff/+NN954w6w2+vXrh3Llyulf68703rlzJ899dJ/9nj17Cn1r3rlz55CUlITRo0cbPDfXuXNnhIWF6T+rhw8f4tKlSxg8eLDBmfyWLVuiTp06Jtvu3bu3wR1DQOE/xz59+hj8vdCdcX7jjTfg6OhoUJ6dnW3yMQEiIiqaIUOGGIynLl68iJs3b2LgwIFISUnRj22ePHmCtm3b4tixY/p/hywxDntWv379kJSUZPDY4ffffw+tVot+/frpy549blZWFh49eoRXX30VAIp0XFOePUZ6ejoePXqEli1b4s6dO0hPTzfY1pzx1I4dO9C8eXOUK1fOYMwYEREBjUZjcLXdHLpx+e+//w7g6ZX45s2bo3nz5jh+/DiAp+OTtLQ0/TijqN555x2jY6ekpOhvic+POeM/naJ+FzUaDX799Vf06NEDwcHB+v1r1KiByMjIAmP84YcfUK9ePYPPUKe4Y+3n7+YYN24cABiNVZ/P5zQaDf73v/+hR48eqFy5sr48ICAAAwcOxIkTJ4ze/xEjRhjc6Tlq1Cg4OjoaHOvZ91epVOLRo0do3rw5MjIycP36dYP2CvPZWYpYLEZUVBT27t0LpVKpL9+yZQtee+01hIaGmt9WSQRYUvLq2E8//YRXX30VLi4u8Pb2hlwux+rVq43+COXl2V8IAPoE5PlnMZ6lS/arVatmVGfqmYfixpiZmYmZM2fqn0UqX7485HI50tLSzG7DVKwvvfSS0fNfjo6ORo8N3Lx5E+np6fD19YVcLjf4UalUSEpKApD3+yKXyw0SO1Nu374NABZdx12natWqRn+oXnrpJQAw6v/z3zNdn57/XJ2dnVG5cmWjEz+BgYFwc3Mr8FibNm1C3bp14eLiAh8fH8jlcuzbt8/k52nuZ2dJuj80u3fv1j8/tmXLFri4uOifBypIUX63+vXrh6ZNm+Ktt96Cn58f+vfvj+3bt5uV3Of1WQFAWFiYvl7336pVqxptZ6oMyPvvT2E+x+ffD11yHxQUZLI8v/eJiIgK5/m/4zdv3gTwNLl6fmzz9ddfQ61W6/+WW2Ic9izdc9LPPt65bds21K9fXz9mAIDU1FRMmDABfn5+kEqlkMvl+n4U5bimnDx5EhEREXBzc4OXlxfkcrl+3prnj2HOeOrmzZvYv3+/0XsaEREBAPoxo7kaNmwIV1dXffKuS+hbtGiBc+fOISsrS1/XrFmzwnX+OUUZt+iYO/4Div5dTE5ORmZmptn5x/Nu375dIuNswHisWqVKFYjF4gL7npycjIyMDJPx16hRA1qtFvfv38/3WO7u7ggICDA41pUrV9CzZ094enpCJpNBLpfrL0iZOlFl7mdnSYMHD0ZmZqZ+DrYbN27g/PnzBc6k/zybeYbeHM+eadE5fvw4unXrhhYtWmDVqlUICAiAk5MTNmzYgJiYGLPazWu2QkEQihWvJWMcN24cNmzYgIkTJyI8PByenp4QiUTo37+/xWfGlEgkRrOga7Va+Pr6GlytfdbzVy7tmanvmaV9++23GDp0KHr06IGpU6fC19dXP9Gc7sSGLRg8eDA+++wz7N69GwMGDEBMTAy6dOlicJU5P0X53ZJKpTh27BgOHz6Mffv2Yf/+/di2bRvatGmD//3vfwXOLlpSTH0vCvs55hV7Sf8NIiIi47/juvHTZ599ZjTniY7uLi5Lj8MkEgl69OiBXbt2YdWqVUhMTMTJkycxf/58g+369u2L33//HVOnTkX9+vXh7u4OrVaLDh06WGT8d/v2bbRt2xZhYWFYtGgRgoKC4OzsjJ9//hmLFy8u0jG0Wi3atWuHadOmmax/9oSFOZycnNCkSRMcO3YMt27dQkJCApo3bw4/Pz/k5OTgzJkzOH78OMLCwoo9Hi2tf4+L+l1Uq9UWjaMk5XXFvzTG2WlpaWjZsiVkMhnmzJmDKlWqwMXFBRcuXMB7771nMytc1KxZE40aNcK3336LwYMH49tvv4WzszP69u1bqHZsKqEvyq0eP/zwA1xcXPDrr79CIpHoyzds2GDJ0IyEhIQA+P8zas+6ceNGkWPM6z34/vvvMWTIEHzxxRf6sqysrDxnWTfFVKz//POPwYzeealSpQp+++03NG3aNN9fxGffl2dvm0lOTi7w7KZuYpLLly/rz+KaUpTvya1btyAIgsG+//zzDwAU2H9dn27cuGHQp+zsbMTGxhrF+vDhQzx58sTgTN/zx/r+++9RuXJl7Ny50yCmvCbAKM5nV5D83s/atWujQYMG2LJlCypWrIh79+5h+fLlxT5mQcRiMdq2bYu2bdti0aJFmD9/Pj788EMcPnwYERERecb87GfVpk0bg7obN27o63X/vXXrllEbpsryUtjPkYiIbIdu3CGTyfIddwCWGYc9r1+/fti0aRMOHjyIa9euQRAEg9vtHz9+jIMHD2L27NmYOXOmvtzUmKCofvzxR6jVauzdu9fg6nReq+6YM56qUqUKVCpVge9pYTRv3hwLFizAb7/9hvLlyyMsLAwikQi1atXC8ePHcfz4cYOJxfJiiUkE82LO+C8v5n4X5XI5pFKpWflHXsd5fpWk5xX1Pbp586bB1fdbt25Bq9UW2He5XA5XV1eT8V+/fh1isdjoTsabN2+idevW+tcqlQrx8fHo1KkTgKerCaSkpGDnzp1o0aKFfrvY2FiTMRTnsytIQe/n4MGDMXnyZMTHxyMmJgadO3cu8K7m59nULfe6N7EwfxwdHBwgEokMliC4e/cudu/ebeHoDAUEBKB+/frYtGmTwW0bBw4cwNWrV4sco5ubm8n+Ozg4GJ0dXL58eZ5LL5iye/dug2dy//jjD5w5cwYdO3YscN++fftCo9Fg7ty5RnW5ubn6mCMiIuDk5ITly5cbxGvOjKYNGzZEaGgolixZYvQePNtWUb4nDx8+NFhSUKFQYPPmzahfvz78/f3z3TciIgLOzs5YtmyZQRzr1q1Denq60UoKubm5BsuHZGdn48svv4RcLkejRo0A/P8Z4GfbO3PmDE6dOmUyhuJ8dgVxc3PL97a9QYMG4X//+x+WLFkCHx8fixwzP6aWa9Odrdadmc7rO/Dyyy/D19cXa9asMTiL/csvv+DatWv6zyowMBC1a9fG5s2b9cvbAMDRo0dx6dIls2Mt7OdIRES2o1GjRqhSpQo+//xzg38LdJ5dwtgS47DnRUREwNvbG9u2bcO2bdvwyiuvGCREpv6NAcwbU5nL1DHS09PzvDBmzniqb9++OHXqFH799Vej/dPS0pCbm1voOJs3bw61Wo0lS5agWbNm+iSpefPm+Oabb/Dw4UOznp/Pa5xtCeaM//Ji7nfRwcEBkZGR2L17N+7du6evv3btmsn3+3m9e/fGX3/9ZXKZbd13oCjjbAD6ZaV1dBeACho3Ojg4oH379tizZ4/B7e2JiYmIiYlBs2bNIJPJDPZZu3atwXJvq1evRm5urv5Ypr7X2dnZWLVqlckYivPZFaSg93PAgAEQiUSYMGEC7ty5Y/Y8Vc+yqSv0ujfsww8/RP/+/eHk5ISuXbsaPdPwrM6dO2PRokXo0KEDBg4ciKSkJKxcuRJVq1bF33//XaLxRkdHo3PnzmjWrBnefPNNpKamYvny5ahVq5bBL2NhYmzUqBF+++03LFq0CIGBgQgNDUWTJk3QpUsXfPPNN/D09ETNmjVx6tQp/Pbbb/Dx8TE73qpVq6JZs2YYNWqU/o+ij49PnrdEPatly5YYOXIkoqOjcfHiRbRv3x5OTk64efMmduzYgaVLl+L111+HXC7Hu+++i+joaHTp0gWdOnXCn3/+iV9++UW/FGFexGIxVq9eja5du6J+/foYNmwYAgICcP36dVy5ckX/h0r3PRk/fjwiIyPh4OCA/v3759v2Sy+9hOHDh+Ps2bPw8/PD+vXrkZiYaNadHHK5HNOnT8fs2bPRoUMHdOvWDTdu3MCqVavQuHFjo1+8wMBALFiwAHfv3sVLL72Ebdu24eLFi1i7dq1+Ao8uXbpg586d6NmzJzp37ozY2FisWbMGNWvWNPmHvDifXUEaNWqEbdu2YfLkyWjcuDHc3d3RtWtXff3AgQMxbdo07Nq1C6NGjcpzuTlLmTNnDo4dO4bOnTsjJCQESUlJWLVqFSpWrKh/Nq5KlSrw8vLCmjVr4OHhATc3NzRp0gShoaFYsGABhg0bhpYtW2LAgAH6ZesqVaqESZMm6Y8zf/58dO/eHU2bNsWwYcPw+PFjrFixArVr1zb5GZhS2M+RiIhsh1gsxtdff42OHTuiVq1aGDZsGCpUqIB///0Xhw8fhkwmw48//ggAFhmHPc/JyQm9evXC1q1b8eTJE3z++ecG9TKZDC1atMDChQuRk5ODChUq4H//+1+eVxmLon379nB2dkbXrl0xcuRIqFQqfPXVV/D19UV8fLzR9uaMp6ZOnYq9e/eiS5cuGDp0KBo1aoQnT57g0qVL+P7773H37t0Cx4TPCw8Ph6OjI27cuKFfcg54uizY6tWrAcCshD6vcbYlmDP+y0thvouzZ8/G/v370bx5c4wePRq5ubn6/KOg3Gfq1Kn4/vvv0adPH7z55pto1KgRUlNTsXfvXqxZswb16tXLd4yVn9jYWHTr1g0dOnTAqVOn8O2332LgwIGoV69ege/dvHnzcODAATRr1gyjR4+Go6MjvvzyS6jVaixcuNBo++zsbLRt2xZ9+/bVj8mbNWuGbt26AXi6zF65cuUwZMgQjB8/HiKRCN98802ej04U57MrSP369eHg4IAFCxYgPT0dEokEbdq0ga+vL4CneUaHDh2wY8cOeHl5mbXkuhGz58MvJXPnzhUqVKggiMVig+U3AAhjxowxuc+6deuEatWqCRKJRAgLCxM2bNhg1jICuqUPzp49a7CduUuOCIIg/PDDD0KNGjUEiUQi1KxZU9i5c6cwZMgQo2XAzI3x+vXrQosWLQSpVGqwzNvjx4+FYcOGCeXLlxfc3d2FyMhI4fr160Z9MkW3zMJnn30mfPHFF0JQUJAgkUiE5s2bGywlIQhPl61zc3PLs621a9cKjRo1EqRSqeDh4SHUqVNHmDZtmvDw4UP9NhqNRpg9e7YQEBAgSKVSoVWrVsLly5fNWkJNEAThxIkTQrt27QQPDw/Bzc1NqFu3rrB8+XJ9fW5urjBu3DhBLpcLIpGowCXsQkJChM6dOwu//vqrULduXf1nsGPHDoPt8vo+6KxYsUIICwsTnJycBD8/P2HUqFFGy+u1bNlSqFWrlnDu3DkhPDxccHFxEUJCQoQVK1YYbKfVaoX58+cLISEhgkQiERo0aCD89NNPRt+dwnx2RV22TqVSCQMHDhS8vLxMLrkoCILQqVMnAYDw+++/m3xvnvds3M/Dc0vUPB/3wYMHhe7duwuBgYGCs7OzEBgYKAwYMMBo+Zs9e/YINWvWFBwdHY2WEdm2bZvQoEEDQSKRCN7e3kJUVJTBsn86W7duFcLCwgSJRCLUrl1b2Lt3r9C7d28hLCzMrL4U5XN8lu7zKOx3kYiIzJfX31qdP//8U+jVq5fg4+MjSCQSISQkROjbt69w8OBB/TbmjsMKM4YUBEE4cOCAAEAQiUTC/fv3jeofPHgg9OzZU/Dy8hI8PT2FPn36CA8fPjT6t7Q4y9bt3btXqFu3ruDi4iJUqlRJWLBggbB+/Xqj9swdTwmCICiVSmH69OlC1apVBWdnZ6F8+fLCa6+9Jnz++ecGy40934/8NG7cWAAgnDlzxuD9ASAEBQWZ1de8xtm6bZ9fMtfc99Xc8Z8lvouCIAhHjx4VGjVqJDg7OwuVK1cW1qxZY9Y4UBAEISUlRRg7dqxQoUIFwdnZWahYsaIwZMgQ4dGjR/pt8htjPU933KtXrwqvv/664OHhIZQrV04YO3aswRLCgpB/PnfhwgUhMjJScHd3F1xdXYXWrVsbjTt1n8fRo0eFESNGCOXKlRPc3d2FqKgoISUlxWDbkydPCq+++qoglUqFwMBAYdq0afolmZ/9/TT3syvqsnWCIAhfffWVULlyZf3Sgs//fdi+fbsAQBgxYoTJ96YgIkHgrEsvurt37yI0NBSfffYZ3n33XWuHU+oqVaqE2rVr46effrJ2KHarZ8+euHTpUqGeL7dX9evXh1wux4EDB6wdChEREdmBVq1a4dGjRwU+n/4imjVrFmbPno3k5ORC33lBT+3Zswc9evTAsWPHirT0ok09Q09Etic+Ph779u0r9BIati4nJ8foOb4jR47gr7/+QqtWrawTFBERERGVKV999RUqV65c5GUXbeoZeiKyHbGxsTh58iS+/vprODk5YeTIkdYOyaL+/fdfRERE4I033kBgYCCuX7+ONWvWwN/fH++88461wyMiIiKiF9jWrVvx999/Y9++fVi6dGmRVxhgQk9EJh09ehTDhg1DcHAwNm3aVOBqAPamXLlyaNSoEb7++mskJyfDzc0NnTt3xqefflqsSY6IiIiIiAoyYMAAuLu7Y/jw4Rg9enSR2+Ez9ERERERERER2iM/QExEREREREdkhJvREREREREREdojP0APQarV4+PAhPDw8ijwZARG9eARBgFKpRGBgIMRinv8kIiqLOE4korzYwliRCT2Ahw8fIigoyNphEJGNun//PipWrGjtMIiIyAo4TiSiglhzrMiEHoCHhweApx+ETCazcjRPabVaJCcnQy6Xl6krg+x32em3PfRZoVAgKChI/zeCiIjKHlsbJ9rDv58lpaz2vaz2G7D9vtvCWJEJPaC/fUomk9nEH2rg6Zc3KysLMpnMJr+8JYX9Ljv9tqc+8xZLIqKyy9bGifb076elldW+l9V+A/bTd2uOFW33XSEiIiIiIiKiPDGhJyIiIiIiIrJDTOiJiIiIiIiI7BCfoSciIiIiKoM0Gg1ycnKsHYbZtFotcnJykJWVZdPPU1taWe03YP2+Ozk5wcHBodSPWxhM6ImIiIiIyhBBEJCQkIC0tDRrh1IogiBAq9VCqVSWqQlry2q/Advou5eXF/z9/W32vWdCT0RERERUhuiSeV9fX7i6utpsovI8QRCQm5sLR0dHu4nZEspqvwHr9l0QBGRkZCApKQkAEBAQUKrHNxcTeiIiIiKiMkKj0eiTeR8fH2uHUyhlNbEtq/0GrN93qVQKAEhKSoKvr69N3n5fth7CICIiIiIqw3TPzLu6ulo5EiL7oPtdsdX5JpjQExERERGVMWXtSi9RUdn67woTeiIiIiIiIiI7xGfoiYiIiIgIycnJUCgUpXY8mUwGuVxeasezdZUqVcLEiRMxceLEEjvGkSNH0Lp1azx+/BheXl55bnfw4EGMHTsWly9fhoODA2bNmoXdu3fj4sWLZh9LJBJh165d6NGjR5HjHTZsGFJTU7Fnz54it5Gf/fv34/3338eFCxfsdklAJvRFVNAfPP6BIiIiIiqbSjIxFgQBGo0Gvr6+Fm03OTkZAwcOREpKikXbzY+Pjw9iYmLMHjMPGzYMmzZtwvz58zF9+nR9+e7du9GzZ08IgmD2sfNKnr/66iusWLECt2/fhqOjI0JDQ9G3b1+D45WUs2fPws3NrcSPY45p06bho48+sslJ4AojNTUV48aNw48//gixWIzevXtj6dKlcHd3BwB06NABM2bMwJYtWzBo0CArR1s0TOiLIDk5GW+OeAfKzKw8t/GQumD92jVM6omIiIjKkJJOjEUiERo2bIhPPvnEokm9QqFASkoKJBKJfmbvkpSZmYmUlBQoFIpCjZddXFywcOFCvPPOOyhXrpxFY1q/fj0mTpyIZcuWoWXLllCr1fj7779x+fLlYrWbnZ0NZ2fnArezlbzhxIkTuH37Nnr37m3tUIotKioK8fHxOHDgAHJycjBs2DCMGDECMTEx+m2GDh2KZcuW2W1Cb5/3FViZQqGAMjMLrQaNQu9pnxj9tBo0CsrMrFK9ZYmIiIiIrO/ZxNjLy8viPxKJBEqlssTGmVKpFG5ubiX+U9STBm3atIG/vz+io6Pz3e6HH35ArVq1IJFIUKlSJXzxxRf6ulatWiEuLg6TJk2CSCTST3q2d+9e9O3bF8OHD0fVqlVRq1YtDBgwAJ988onBvs9f1e/RoweGDh2qf12pUiXMnTsXgwcPhkwmw4gRI/Daa6/hvffeM9gvOTkZTk5OOHbsmH6/JUuWAAAGDhyIfv36GWyfk5OD8uXLY/PmzQAArVaL6OhohIaGQiqVol69evj+++8N9vn555/x0ksvQSqVonXr1rh7926+7xsAbN26Fe3atYOLi0ue25w9exbt2rVD+fLl4enpiZYtW+LChQtG28XHx6Njx46QSqWoXLmyUXz3799H37594eXlBW9vb3Tv3t2sGM1x7do17N+/H19//TWaNGmCZs2aYfny5di6dSsePnyo365r1644d+4cbt++bZHjljYm9MXgE1AR/iGhRj8+ARWtHRoRERERWVFJJcalcfXcljk4OOCTTz7B8uXL8eDBA5PbnD9/Hn379kX//v1x6dIlzJo1CzNmzMDGjRsBADt37kTFihUxZ84cxMfHIz4+HgDg7++P06dPIy4urthxfv7556hXrx7+/PNPzJgxA1FRUdi6davBYwHbtm1DYGAgmjdvbrR/VFQUfvzxR6hUKn3Zr7/+ioyMDPTs2RMAEB0djc2bN2PNmjW4cuUKJk2ahDfeeANHjx4F8DRZ7tWrF7p27YqLFy/irbfewvvvv19g7MePH8fLL7+c7zZKpRJDhgzBiRMncPr0aVSrVg2dOnWCUqk02G7GjBno3bs3/vrrL0RFRaF///64du0agKcnKCIjI+Hh4YHjx4/j5MmTcHd3R4cOHZCdnW3yuPPnz4e7u3u+P/fu3QMAnDp1Cl5eXgZ9iYiIgFgsxpkzZ/RlwcHB8PPzw/Hjxwt8b2wRb7knIiIiIiK70bNnT9SvXx8ff/wx1q1bZ1S/aNEitG3bFjNmzAAAvPTSS7h69So+++wzDB06FN7e3nBwcICHhwf8/f31+3388cfo1asXKlWqhJdeegnh4eHo1KkTXn/99UJPmNamTRtMmTJF/7pv376YOHEiTpw4oU/gY2JiMGDAAJPLokVGRsLNzQ27du3CG2+8AQD47rvv0K1bN3h4eECtVmP+/Pn47bffEB4eDgCoXLkyTpw4gS+//BItW7bE6tWrUaVKFf3dCdWrV8elS5ewYMGCfGOPi4tDYGBggf171tq1a+Hl5YWjR4+iS5cu+vI+ffrgrbfeAgDMnTsXBw4cwPLly7Fq1Sps27YNWq0WX3/9tf492LBhA7y8vHDkyBG0b9/e6LjvvPMO+vbtm29sutgTEhKMHktxdHSEt7c3EhISjPaxxIkca+AVeiIiIiIisisLFizApk2b9Fd7n3Xt2jU0bdrUoKxp06a4efMmNBpNnm0GBATg1KlTuHTpEiZMmIDc3FwMGTIEHTp0gFarLVR8z1/hlsvlaN++PbZs2QIAiI2NxalTpxAVFWVyf0dHR/Tt21e//ZMnT7Bnzx799rdu3UJGRgbatWtncHV68+bN+lvHr127hiZNmhi0q0v+85OZmZnv7fYAkJiYiLfffhvVqlWDp6cnZDIZVCqV/up4XscLDw/Xf2Z//fUXbt26BQ8PD3383t7eyMrKyvP2d29vb1StWjXfH0fHwl+zlkqlyMjIKPR+toBX6ImIiIiIyK60aNECkZGRmD59usHz65ZQu3Zt1K5dG6NHj8Y777yD5s2b4+jRo2jdujXEYrHRbPo5OTlGbZiarT4qKgrjx4/H8uXLERMTgzp16qBOnTp5xhEVFYWWLVsiKSkJ+/fvh1QqRYcOHQBAfyv+vn37UKFCBYP9JBJJofv8rPLly+Px48f5bjNkyBCkpKRg6dKlCAkJgUQiQXh4eJ63ypuiUqnQqFEj/UmLZ+U1QeD8+fMxf/78fNu9evUqgoOD4e/vj6SkJIO63NxcpKamGtyZATydDd9WJiUsLCb0RERERERkdz799FPUr18f1atXNyivUaMGTp48aVB28uRJvPTSS/pl2JydnfO9Wq9Ts2ZNAE+vkANPE03dM/cAoNFocPnyZbRu3brAtrp3744RI0Zg//79iImJweDBg/Pd/rXXXkNQUBC2bduGX375Ba+//jqcnJz0cUkkEty7dw8tW7Y0uX+NGjWwd+9eg7LTp08XGGeDBg1w9erVfLc5efIkVq1ahU6dOgF4+rz+o0ePjLY7ffq0QT9Pnz6NBg0aAAAaNmyIbdu2wdfXFzKZrMC4gMLdch8eHo60tDScP38ejRo1AgAcOnQIWq3W4M4F3R0BurjsDRN6IiIiIiKyO3Xq1EFUVBSWLVtmUD5lyhQ0btwYc+fORb9+/XDq1CmsWLECq1at0m9TqVIlHDt2DP3794dEIkH58uUxatQoBAYGok2bNqhYsSLi4+Mxb948yOVy/a3jbdq0weTJk7Fv3z5UqVIFixYtQlpamlnxurm5oUePHpgxYwauXbuGAQMGFLjPwIED8eWXX+Kff/7BoUOH9OUeHh549913MWnSJGi1WjRr1gzp6ek4efIkZDIZhgwZgnfeeQdffPEFpk6dirfeegvnz5/XTwyYn8jISGzatCnfbapVq4ZvvvkGL7/8MhQKBaZOnWpywsYdO3bg5ZdfRrNmzbBlyxb88ccf+nkPoqKi8Nlnn6F79+6YM2cOKlasiLi4OOzcuRPTpk1DxYrGE417e3vD29u7wD4AT09odOjQAW+//TbWrFmDnJwcjB07Fv379zeYI+D06dP6OwzsEZ+hJyIiIiIiAE+fn37y5EmJ/2RmZlok3jlz5hg9396wYUNs374dW7duRe3atTFz5kzMmTPH4Nb8OXPm4O7du6hSpYr+VuuIiAicPn0affr0wUsvvYTevXvDxcUFBw8ehI+PDwDgzTffxJAhQzB48GC0bNkSlStXNuvqvE5UVBT++usvNG/eHMHBwWZtf/XqVVSoUMFoXoC5c+dixowZiI6O1iev+/btQ2hoKICns7f/8MMP2L17N+rVq4c1a9YUeLu67phXrlzBjRs38txm3bp1ePz4MRo2bIhBgwZh/PjxRhPQAcDs2bOxdetW1K1bF5s3b8Z3332nv+vB1dUVx44dQ3BwMHr16oUaNWpg+PDhyMrKMvuKfUG2bNmCsLAwtG3bFp06dUKzZs2wdu1ag22+++47REVFwdXV1SLHLG0i4fmHQMoghUIBT09PpKenm/XluX37NoaPGY/e0z6Bf0ioUX1CXCx+WPgh1q1chipVqhQpJq1Wi6SkJPj6+hZ6Vk17xn6XnX7bQ58L+7eBiIhePEUZJ/bp0wdeXl4mn6MuroyMDMjlcsybNw9Vq1Yt9P5ZWVmIjY1FaGiowcRnycnJGDhwIFJSUiwZbr58fHwQExNj9rPLgiAgNzcXjo6OJmeGf1FZo99Tp06FQqHAl19+WSrHy0tJ9/3Ro0eoXr06zp07pz8R8ry8fmcA2xgr8pZ7IiIiIqIyTi6XIyYmBgqFotSOKZPJ7HYishfdhx9+iFWrVkGr1drshRdLuHv3LlatWpVnMm8PmNATERERERHkcjkTbAIAeHl54YMPPrB2GCXu5ZdfNlpi0N68uKdbiIiIiIiIiF5gVk3ojx07hq5duyIwMBAikQi7d+/Oc9t33nkHIpEIS5YsMShPTU1FVFQUZDIZvLy8MHz4cP26jERERET0Yvv333/xxhtvwMfHB1KpFHXq1MG5c+f09YIgYObMmQgICIBUKkVERARu3rxpxYiJiCzHqgn9kydPUK9ePaxcuTLf7Xbt2oXTp08bLC+go5uF8cCBA/jpp59w7NgxjBgxoqRCJiIiIiIb8fjxYzRt2hROTk745ZdfcPXqVXzxxRcoV66cfpuFCxdi2bJlWLNmDc6cOQM3NzdERkYiKyvLipETEVmGVZ+h79ixIzp27JjvNv/++y/GjRuHX3/9FZ07dzaou3btGvbv34+zZ8/qn31Yvnw5OnXqhM8//9zkCQAiIiIiejEsWLAAQUFB2LBhg77s2cmtBEHAkiVL8NFHH6F79+4AgM2bN8PPzw+7d+9G//79Sz1mIiJLsulJ8bRaLQYNGoSpU6eiVq1aRvWnTp2Cl5eXwUQGEREREIvFOHPmDHr27GmyXbVaDbVarX+tm81Tq9UarWNpiiAIT5dNEARAMLH9f/WCIJjVnilarbZY+9sr9rvs9Nse+mzLsREREbB3715ERkaiT58+OHr0KCpUqIDRo0fj7bffBgDExsYiISEBERER+n08PT3RpEkTnDp1igk9Edk9m07oFyxYAEdHR4wfP95kfUJCAnx9fQ3KHB0d4e3tjYSEhDzbjY6OxuzZs43Kk5OTzbr9SqlUIjQ4CFJNJkTKVKN6qSYTocFBUCqVSEpKKrA9U7RaLdLT0yEIwgu9VMTz2O+y02976LNSqbR2CERElI87d+5g9erVmDx5Mj744AOcPXsW48ePh7OzM4YMGaIfD/r5+Rns5+fnl+dY0VIXfnQ/lqZrt6gnxXUn1HU/9kYXsz3GXhxltd+A9fuu+10x9TfAFi7+2GxCf/78eSxduhQXLlyw+B/D6dOnY/LkyfrXCoUCQUFBkMvlkMlkBe6vUqkQe+8+GjhI4enhbVSfmapA7L378PDwMDrhYC6tVguRSAS5XG6zyU5JYL/LTr/toc8uLi7WDoGIiPKh1Wrx8ssvY/78+QCABg0a4PLly1izZg2GDBlSpDYtceGnWrVqcHNzK5F/R9RqNWQyGVQqVZEuHOXk5ECr1SI3Nxe5ubkWj68kCYIAjUYDACVyssRWldV+A7bR99zcXGi1WqSkpMDJycmgzhYu/thsQn/8+HEkJSUhODhYX6bRaDBlyhQsWbIEd+/ehb+/v9EfstzcXKSmpsLf3z/PtiUSCSQSiVG5WCw2K7HQnRWFSASITGz/X71IJCpWoqLb31aTnZLCfpedftt6n201LiIieiogIAA1a9Y0KKtRowZ++OEHANCPBxMTExEQEKDfJjExEfXr1zfZpiUu/Ny8eRNeXl5wc3MrbJcKlJGRAblcDnd39yJdOMrKyoJSqYSjoyMcHQ1TgeTkZP0dCaVBJpMVad3755Oq0nDkyBG0adMGqamp8PLyKrHjDBs2DGlpadi1a5dR3bP9Hjx4MMLCwvRrxYeGhmLChAmYOHGiWcfZuHEjJk2ahMePHxcrXrFYjJ07d6JHjx7Faicv77//PpRKZYGTqJckR0dHiMVi+Pj4GJ2ks4WLPzab0A8aNMjgeScAiIyMxKBBgzBs2DAAQHh4ONLS0nD+/Hk0atQIAHDo0CFotVo0adKk1GMmIiIiotLTtGlT3Lhxw6Dsn3/+QUhICICnSY6/vz8OHjyoT+AVCgXOnDmDUaNGmWzTUhd+SuqWdl27Rb1wJBaLTT4SkJycjNEjRkKdkWHJcPMlcXXF6q/Wmp3UJyUlYcaMGfjll1+QmJiIcuXKoV69epg5cyaaNm1aorE2bdoU8fHx8PLyKpUrxc8eQz9/13/lf/31F37++WesXr3aYLvCPObxbHuWiLUo7cTHx2PKlCk4d+4cbt26hfHjxxstUf7uu++iSpUqmDJlCqpUqVLsWItC1z9TfwNs4eKPVRN6lUqFW7du6V/Hxsbi4sWL8Pb2RnBwMHx8fAy2d3Jygr+/P6pXrw7g6RnYDh064O2338aaNWuQk5ODsWPHon///pzhnoiIiOgFN2nSJLz22muYP38++vbtiz/++ANr167F2rVrATwdiE+cOBHz5s1DtWrVEBoaihkzZiAwMLDErijaK4VCAXVGBqb0H4Qg/4CCdyim+wnx+GLrN1AoFGYn9K+//jrUajU2btyIKlWqIDExEQcPHkRKSkqR49Dd0v383QrPc3Z2zvcO4NK0fPly9OnTB+7u7tYOpVjUajXkcjk++ugjLF682OQ25cuXR7t27bB69Wp8/vnnpRyhfbDqKYVz586hQYMGaNCgAQBg8uTJaNCgAWbOnGl2G1u2bEFYWBjatm2LTp06oVmzZvo/4kRERET04mrcuDF27dqF7777DrVr18bcuXOxZMkSREVF6beZNm0axo0bhxEjRqBx48ZQqVTYv3+/Tdwqa4uC/ANQJSi4xH8Ke9IgLS0Nx48fx/z589G6dWuEhITglVdewfTp09GtWzcAwN27dyESiXDx4kWD/UQiEY4cOQLg6a3zIpEIv/zyCxo1agSJRIL169dDJBLh+vXrBsdcvHix/qqwbr+0tDQoFApIpVL88ssvBtvv2rULHh4eyPjvLof79++jb9++8PLygre3N7p37467d+/qt9doNJg8eTK8vLzg4+ODadOmFXhXh0ajwffff4+uXbvmu92iRYtQp04duLm5ISgoCKNHj4ZKpTLabvfu3ahWrRpcXFwQGRmJ+/fvG9Tv2bMHDRs2hIuLCypXrozZs2dbbO6FSpUqYenSpRg8eDA8PT3z3K5z587Ytm2bRY75IrJqQt+qVSuDW5J0Pxs3bjS5/d27d42eC/H29kZMTAyUSiXS09Oxfv16uz9bRURERETm6dKlCy5duoSsrCxcu3ZNv2Sdjkgkwpw5c5CQkICsrCz89ttveOmll6wULRWVu7s73N3dsXfvXoNVCIrq/fffx6effopr167h9ddfx8svv4wtW7YYbLNlyxYMHDjQaF+ZTIYuXbogJibGaPsePXrA1dUVOTk5iIyMhIeHB44fP46TJ0/C3d0dHTp0QHZ2NgDgiy++wMaNG7F+/XqcOHECqampJp+df9bff/+N9PR0g2W7TRGLxVi2bBmuXLmCTZs24dChQ5g2bZrBNhkZGfjkk0+wefNmnDx5EmlpaQZLOR4/fhyDBw/GhAkTcPXqVXz55ZfYuHEjPvnkkzyPW6tWLf1nZeqnY8eO+cZtSuPGjfHgwQODkyH0/2z2GXoiIiIiIiLg6cRkGzZswIgRI7B27Vo0bNgQLVu2RP/+/VG3bt1Ctzdnzhy0a9dO/zoqKgorVqzA3LlzATydi+H8+fP49ttvTe4fFRWFQYMGISMjA66urlAoFNi3b58+Id+2bRu0Wi2+/vpr/fPlGzZsgJeXF44cOYL27dtjyZIlmD59Onr16gUAWLNmDX799dd8446Li4ODg0OBEyI+exG0UqVKmDdvHt555x2sWrVKX56Tk4MVK1bo5x7btGkTatSogT/++AOvvPIKZs+ejffff1+/YkTlypUxd+5cTJs2DR9//LHJ4/7888/IycnJMy6pVJpv3KboHqWOi4tDpUqVCr3/i876T/ETEREREREVoHfv3oiLi8OePXvQoUMHHDlyBA0bNszz7t78PH+Fu3///rh79y5Onz4N4OnV9oYNGyIsLMzk/p06dYKTkxP27t0LAPjhhx8gk8n0k3r/9ddfuHXrFjw8PPRXp729vZGVlYXbt28jPT0d8fHxBhN5Ozo6FnjlPTMzExKJpMBJ6H777Te0bdsWFSpUgIeHBwYNGoSUlBT94wC64zVu3Fj/OiwsDF5eXrh27Zq+D3PmzDG4wv72228jPj7eoJ1nhYSEoGrVqnn+VKhQId+4TdGdBMjrmGUdE3oiIiIiIrILLi4uaNeuHWbMmIHff/8dQ4cO1V8t1s04/uxz6HldLX5+SUF/f3+0adNGfxt9TEyMwVwMz3N2dsbrr79usH2/fv30k+upVCo0atQIFy9eNPj5559/TN7Gb67y5csjIyNDf9u+KXfv3kWXLl1Qt25d/PDDDzh//rx+2bf89nueSqXC7NmzDeK/dOkSbt68meccFCVxy31qaioAFGmJw7KAt9wTEREREZFdqlmzJnbv3g3g/xO++Ph4/aTbz06QV5CoqChMmzYNAwYMwJ07dwyeJ89r+3bt2uHKlSs4dOgQ5s2bp69r2LAhtm3bBl9fX8hkMpP7BwQE4MyZM2jRogUAIDc3F+fPn0fDhg3zPKZu+cWrV6/q//9558+fh1arxRdffKE/ybF9+3aj7XJzc3Hu3Dm88sorAIAbN24gLS0NNWrU0Pfhxo0bqFq1ar7vw7NK4pb7K1euwMnJCbVq1Sr0vmUBE3oiIiIiIrJpKSkp6NOnDwYPHowGDRpAJpPh3LlzWLhwIbp37w7gabL46quv4tNPP0VoaCiSkpLw0UcfmX2MXr16YdSoURg1ahRat25d4DLYLVq0gL+/P6KiohAaGmpw+3xUVBQ+++wzdO/eHXPmzEHFihURFxeHnTt3Ytq0aahYsSImTJiATz/9FNWqVUNYWBgWLVqEtLS0fI8pl8vRsGFDnDhxIs+EvmrVqsjJycHy5cvRtWtXnDx5EmvWrDHazsnJCePGjcOyZcvg6OiIsWPH4tVXX9Un+DNnzkSXLl0QHByM119/HWKxGH/99RcuX75scPLiWSEhIfnG/zzdCReVSoXk5GRcvHgRzs7OqFmzpn6bEydOoHnz5kU6GVAWMKEnIiIiIiIAT9eHt8XjuLu745VXXsGyZctw584d5OTkICgoCG+//TY++OAD/Xbr16/H8OHD0ahRI1SvXh0LFy5E+/btzTqGh4cHunbtiu3bt2P9+vUFbi8SiTBgwAAsXLjQaNltV1dXHDt2DO+99x569eoFpVKJChUqoG3btvor9lOmTEF8fDyGDBkCsViMN998Ez179kR6enq+x33rrbewefNmjB071mR9vXr1sGjRIixYsADTp09HixYtEB0djcGDBxvF+N5772HgwIH4999/0bx5c6xbt05fHxkZiZ9++glz5szBggUL4OTkhLCwMLz11lsFvjfm0t1JATy9syAmJgYhISEGM9pv374ds2bNstgxXzQioaDFDssAhUIBT09PpKen53lLzLNu376N4WPGo/e0T+AfEmpUnxAXix8Wfoh1K5fp164sLK1Wi6SkJPj6+upvlSkL2O+y02976HNh/zYQEdGLpyjjxD59+sDLy8voOW1LyMjIgFwux7x58wp1K7ROVlYWYmNjERoaavAcdHJyMka9PQLqUpx4TOLqitVfrTX72WhBEJCbmwtHR8cCJ4V7kTzf78zMTFSvXh3btm1DeHi4tcMrUT///DOmTJmCv//+G05OTlaJIa/fGcA2xoq8Qk9EREREVMbJ5XKs/motFApFqR1TJpNxorMikEql2Lx5Mx49emTtUErckydP8PXXX+snGyRjfGeIiIiIiAhyuZwJtp1o1aqVtUMoFa+//jpyc3OtHYZNs837XImIiIiIiIgoX0zoiYiIiIiIiOwQE3oiIiIiIiIiO8SEnoiIiIiojNFqtdYOgcgu2PrvCifFIyIiIiIqI5ydnSEWi/Hw4UPI5XI4OzvbzRJwXLaubPUbsG7fBUFAdnY2kpOTIRaL4ezsXKrHNxcTeiIiIiKiMkIsFiM0NBTx8fF4+PChtcMpFEEQoNVqIRaLy1RiW1b7DdhG311dXREcHAyx2DZvbmdCT0RERERUhjg7OyM4OBi5ubnQaDTWDsdsWq0WKSkp8PHxsdnkqiSU1X4D1u+7g4ODzd8ZwYSeiIiIiKiMEYlEcHJygpOTk7VDMZtWq4WTkxNcXFzKVGJbVvsNlO2+m4vvChEREREREZEdYkJPREREREREZIeY0BMRERERERHZISb0RERERERERHaICT0RERERERGRHWJCT0RERERERGSHmNATERERERER2SEm9ERERERERER2iAk9ERERERERkR1iQk9ERERERERkh5jQExEREREREdkhqyb0x44dQ9euXREYGAiRSITdu3fr63JycvDee++hTp06cHNzQ2BgIAYPHoyHDx8atJGamoqoqCjIZDJ4eXlh+PDhUKlUpdwTIiIiIiIiotJl1YT+yZMnqFevHlauXGlUl5GRgQsXLmDGjBm4cOECdu7ciRs3bqBbt24G20VFReHKlSs4cOAAfvrpJxw7dgwjRoworS4QERERERERWYWjNQ/esWNHdOzY0WSdp6cnDhw4YFC2YsUKvPLKK7h37x6Cg4Nx7do17N+/H2fPnsXLL78MAFi+fDk6deqEzz//HIGBgSXeByIiIiIiIiJrsGpCX1jp6ekQiUTw8vICAJw6dQpeXl76ZB4AIiIiIBaLcebMGfTs2dNkO2q1Gmq1Wv9aoVAAALRaLbRabYFxCIIAkUgECAIgmNj+v3pBEMxqzxStVlus/e0V+112+m0Pfbbl2IiIiIiI7Cahz8rKwnvvvYcBAwZAJpMBABISEuDr62uwnaOjI7y9vZGQkJBnW9HR0Zg9e7ZReXJyMrKysgqMRalUIjQ4CFJNJkTKVKN6qSYTocFBUCqVSEpKKrA9U7RaLdLT0yEIAsTisjN3IftddvptD31WKpXWDoGIiIiIKE92kdDn5OSgb9++EAQBq1evLnZ706dPx+TJk/WvFQoFgoKCIJfL9ScL8qNSqRB77z4aOEjh6eFtVJ+ZqkDsvfvw8PAwOuFgLq1WC5FIBLlcbrPJTklgv8tOv+2hzy4uLtYOgYiIiIgoTzaf0OuS+bi4OBw6dMgg4fb39ze6Ap6bm4vU1FT4+/vn2aZEIoFEIjEqF4vFZiUWutvpIRIBIhPb/1cvEomKlajo9rfVZKeksN9lp9+23mdbjYuIiIiICLDxdeh1yfzNmzfx22+/wcfHx6A+PDwcaWlpOH/+vL7s0KFD0Gq1aNKkSWmHS0RERESlaNasWRCJRAY/YWFh+vqsrCyMGTMGPj4+cHd3R+/evZGYmGjFiImILMuqV+hVKhVu3bqlfx0bG4uLFy/C29sbAQEBeP3113HhwgX89NNP0Gg0+ufivb294ezsjBo1aqBDhw54++23sWbNGuTk5GDs2LHo378/Z7gnIiIiKgNq1aqF3377Tf/a0fH/h7eTJk3Cvn37sGPHDnh6emLs2LHo1asXTp48aY1QiYgszqoJ/blz59C6dWv9a91z7UOGDMGsWbOwd+9eAED9+vUN9jt8+DBatWoFANiyZQvGjh2Ltm3bQiwWo3fv3li2bFmpxE9ERERE1uXo6GjyUcv09HSsW7cOMTExaNOmDQBgw4YNqFGjBk6fPo1XX321tEMlIrI4qyb0rVq1evoseh7yq9Px9vZGTEyMJcMiIiIiIjtx8+ZNBAYGwsXFBeHh4YiOjkZwcDDOnz+PnJwcRERE6LcNCwtDcHAwTp06lWdCb6nljXU/lqZr19aXfi0J9rDkbUkoq/0GbL/vthCXzU+KR0RERERkSpMmTbBx40ZUr14d8fHxmD17Npo3b47Lly8jISEBzs7O8PLyMtjHz8+vxJc3rlatGtzc3EpktRS1Wg2ZTAaVSlXk5ZHtlT0seVsSymq/Advvuy0sccyEnoiIiIjsUseOHfX/X7duXTRp0gQhISHYvn07pFJpkdq0xPLGN2/ehJeXF9zc3IoUQ34yMjIgl8vh7u5e5OWR7ZU9LHlbEspqvwHb77stLHHMhJ6IiIiIXgheXl546aWXcOvWLbRr1w7Z2dlIS0szuEqfmJhYKssb634sTdducZdHtle2vuRtSSmr/QZsu++2EJP1IyAiIiIisgCVSoXbt28jICAAjRo1gpOTEw4ePKivv3HjBu7du4fw8HArRklEZDm8Qk9EREREdundd99F165dERISgocPH+Ljjz+Gg4MDBgwYAE9PTwwfPhyTJ0+Gt7c3ZDIZxo0bh/DwcM5wT0QvDCb0RERERGSXHjx4gAEDBiAlJQVyuRzNmjXD6dOnIZfLAQCLFy/WL2usVqsRGRmJVatWWTlqIiLLYUJPRERERHZp69at+da7uLhg5cqVWLlyZSlFRERUuvgMPREREREREZEdYkJPREREREREZIeY0BMRERERERHZISb0RERERERERHaICT0RERERERGRHWJCT0RERERERGSHmNATERERERER2SEm9ERERERERER2iAk9ERERERERkR1iQk9ERERERERkh5jQExEREREREdkhJvREREREREREdogJPREREREREZEdYkJPREREREREZIeY0BMRERERERHZISb0RERERERERHaICT0RERERERGRHWJCT0RERERERGSHmNATERERERER2SEm9ERERERERER2iAk9ERERERERkR2yakJ/7NgxdO3aFYGBgRCJRNi9e7dBvSAImDlzJgICAiCVShEREYGbN28abJOamoqoqCjIZDJ4eXlh+PDhUKlUpdgLIiIiIiIiotJn1YT+yZMnqFevHlauXGmyfuHChVi2bBnWrFmDM2fOwM3NDZGRkcjKytJvExUVhStXruDAgQP46aefcOzYMYwYMaK0ukBERERERERkFY7WPHjHjh3RsWNHk3WCIGDJkiX46KOP0L17dwDA5s2b4efnh927d6N///64du0a9u/fj7Nnz+Lll18GACxfvhydOnXC559/jsDAwFLrCxERERGZJyUlBTNnzsThw4eRlJQErVZrUJ+ammqlyIiI7ItVE/r8xMbGIiEhAREREfoyT09PNGnSBKdOnUL//v1x6tQpeHl56ZN5AIiIiIBYLMaZM2fQs2dPk22r1Wqo1Wr9a4VCAQDQarVG/6CYIggCRCIRIAiAYGL7/+oFQTCrPVO0Wm2x9rdX7HfZ6bc99NmWYyMismeDBg3CrVu3MHz4cPj5+T0dVxERUaHZbEKfkJAAAPDz8zMo9/Pz09clJCTA19fXoN7R0RHe3t76bUyJjo7G7NmzjcqTk5MNbufPi1KpRGhwEKSaTIiUxmeQpZpMhAYHQalUIikpqcD2TNFqtUhPT4cgCBCLy87chex32em3PfRZqVRaOwQiohfS8ePHceLECdSrV8/aoRAR2TWbTehL0vTp0zF58mT9a4VCgaCgIMjlcshksgL3V6lUiL13Hw0cpPD08Daqz0xVIPbefXh4eBidcDCXVquFSCSCXC632WSnJLDfZaff9tBnFxcXa4dARPRCCgsLQ2ZmprXDICKyezab0Pv7+wMAEhMTERAQoC9PTExE/fr19ds8fwU8NzcXqamp+v1NkUgkkEgkRuVisdisxEJ3Oz1EIkBkYvv/6kUiUbESFd3+tprslBT2u+z029b7bKtxERHZu1WrVuH999/HzJkzUbt2bTg5ORnUm3OBhYiIbHgd+tDQUPj7++PgwYP6MoVCgTNnziA8PBwAEB4ejrS0NJw/f16/zaFDh6DVatGkSZNSj5mIiIiICubl5QWFQoE2bdrA19cX5cqVQ7ly5eDl5YVy5cpZOzwiIrth1Sv0KpUKt27d0r+OjY3FxYsX4e3tjeDgYEycOBHz5s1DtWrVEBoaihkzZiAwMBA9evQAANSoUQMdOnTA22+/jTVr1iAnJwdjx45F//79OcM9ERERkY2KioqCk5MTYmJiOCkeEVExWDWhP3fuHFq3bq1/rXuufciQIdi4cSOmTZuGJ0+eYMSIEUhLS0OzZs2wf/9+g+dat2zZgrFjx6Jt27YQi8Xo3bs3li1bVup9ISIiIiLzXL58GX/++SeqV69u7VCIiOyaVRP6Vq1aPX0WPQ8ikQhz5szBnDlz8tzG29sbMTExJREeEREREZWAl19+Gffv32dCT0RUTDY7KR4RERERvZjGjRuHCRMmYOrUqahTp47RpHh169a1UmRERPaFCT0RERERlap+/foBAN588019meiZVYI0Go21QiMisis2O8s9EREREb2YYmNjjX7u3Lmj/29RffrppxCJRJg4caK+LCsrC2PGjIGPjw/c3d3Ru3dvJCYmWqAXRETWxyv0RERERFSqQkJCLN7m2bNn8eWXXxrdrj9p0iTs27cPO3bsgKenJ8aOHYtevXrh5MmTFo+BiKi0MaEnIiIiolK1efPmfOsHDx5cqPZUKhWioqLw1VdfYd68efry9PR0rFu3DjExMWjTpg0AYMOGDahRowZOnz6NV199tfDBExHZECb0RERERFSqJkyYYPA6JycHGRkZcHZ2hqura6ET+jFjxqBz586IiIgwSOjPnz+PnJwcRERE6MvCwsIQHByMU6dOMaEnIrvHhJ6IiIiIStXjx4+Nym7evIlRo0Zh6tSphWpr69atuHDhAs6ePWtUl5CQAGdnZ3h5eRmU+/n5ISEhwWR7arUaarVa/1qhUAAAtFottFptgfHoJvbT/Viarl1BEMyK50Wi1WrZ7zLG1vtuC3ExoSciIiIiq6tWrRo+/fRTvPHGG7h+/bpZ+9y/fx8TJkzAgQMH4OLiYpE4oqOjMXv2bKPy5ORkZGVlFbi/UqlEtWrV4ObmZrGYnqVWqyGTyaBSqZCUlGTx9m2ZVqtFeno6BEGAWFx25vYuq/0GbL/vSqXS2iEwoSciIiIi2+Do6IiHDx+avf358+eRlJSEhg0b6ss0Gg2OHTuGFStW4Ndff0V2djbS0tIMrtInJibC39/fZJvTp0/H5MmT9a8VCgWCgoIgl8shk8kKjEmlUuHmzZvw8vKCm5ub2X0xV0ZGBuRyOdzd3eHr62vx9m2ZVquFSCSCXC63yeSupJTVfgO23/eSOGlXWEVK6M+ePYvDhw8jKSnJ6DaDRYsWWSQwIiIiInox7d271+C1IAiIj4/HihUr0LRpU7Pbadu2LS5dumRQNmzYMISFheG9995DUFAQnJyccPDgQfTu3RsAcOPGDdy7dw/h4eEm25RIJJBIJEblYrHYrIRCdzu87sfSdO2KRCKbTHBKmq7fZa3vZbXfgG333RZiKnRCP3/+fHz00UeoXr06/Pz8DJ4NKonnhIiIiIjoxdKjRw+D17orcG3atMEXX3xhdjseHh6oXbu2QZmbmxt8fHz05cOHD8fkyZPh7e0NmUyGcePGITw8nBPiEdELodAJ/dKlS7F+/XoMHTq0BMIhIiIiohddaU4ktXjxYojFYvTu3RtqtRqRkZFYtWpVqR2fiKgkFTqhF4vFhboVioiIiIiotBw5csTgtYuLC1auXImVK1daJyAiohJU6IR+0qRJWLlyJZYsWVIC4RARERHRi06j0WDjxo04ePCgyTmZDh06ZKXIiIjsS6ET+nfffRedO3dGlSpVULNmTTg5ORnU79y502LBEREREdGLZ8KECdi4cSM6d+6M2rVrcx4mIqIiKnRCP378eBw+fBitW7eGj48P/wATERERUaFs3boV27dvR6dOnawdChGRXSt0Qr9p0yb88MMP6Ny5c0nEQ0REREQvOGdnZ1StWtXaYRAR2b1CL5zn7e2NKlWqlEQsRERERFQGTJkyBUuXLi2RddqJiMqSQl+hnzVrFj7++GNs2LABrq6uJRETEREREb3ATpw4gcOHD+OXX35BrVq1OCcTEVERFTqhX7ZsGW7fvg0/Pz9UqlTJ6A/whQsXLBYcEREREb14vLy80LNnT2uHQURk9wqd0Pfo0aMEwiAiIiKismLDhg3WDoGI6IVQ6IT+448/Lok4iIiIiIiIiKgQCj0pHhERERERERFZX6Gv0IvF4nzXntdoNMUKiIiIiIiIiIgKVuiEfteuXQavc3Jy8Oeff2LTpk2YPXu2xQIjIiIiIiIiorwVOqHv3r27Udnrr7+OWrVqYdu2bRg+fLhFAiMiIiIiIiKivBU6oc/Lq6++ihEjRliqOSIiIiJ6gSxbtgwjRoyAi4sLli1blu+27u7uqFWrFpo0aVJK0RER2SeLJPSZmZlYtmwZKlSoYInmiIiIiOgFs3jxYkRFRcHFxQWLFy/Od1u1Wo2kpCRMmjQJn332WSlFSERkfwqd0JcrV85gUjxBEKBUKuHq6opvv/3WosFpNBrMmjUL3377LRISEhAYGIihQ4fio48+0scgCAI+/vhjfPXVV0hLS0PTpk2xevVqVKtWzaKxEBEREVHRxcbGmvz/vBw4cAADBw5kQk9ElI9CJ/RLliwxeC0WiyGXy9GkSROUK1fOUnEBABYsWIDVq1dj06ZNqFWrFs6dO4dhw4bB09MT48ePBwAsXLgQy5Ytw6ZNmxAaGooZM2YgMjISV69ehYuLi0XjISIiIqLS0axZM3z00UfWDoOIyKYVOqEfMmRIScRh0u+//47u3bujc+fOAIBKlSrhu+++wx9//AHg6dX5JUuW4KOPPtJP1rd582b4+flh9+7d6N+/f6nFSkRERETmO3jwIBYvXoxr164BAGrUqIGJEyciIiICACCVSjFhwgRrhkhEZPOK9Ax9Wloa/vjjDyQlJUGr1RrUDR482CKBAcBrr72GtWvX4p9//sFLL72Ev/76CydOnMCiRYsAPL1dKyEhQf+HHwA8PT3RpEkTnDp1Ks+EXq1WQ61W618rFAoAgFarNeqPKYIgPL3lXxAAwcT2/9ULgmBWe6Zotdpi7W+v2O+y02976LMtx0ZEZM9WrVqFCRMm4PXXX9cn7adPn0anTp2wePFijBkzxsoREhHZh0In9D/++COioqKgUqkgk8kMnqcXiUQWTejff/99KBQKhIWFwcHBARqNBp988gmioqIAAAkJCQAAPz8/g/38/Pz0daZER0dj9uzZRuXJycnIysoqMC6lUonQ4CBINZkQKVON6qWaTIQGB0GpVCIpKanA9kzRarVIT0+HIAgQi8VFasMesd9lp9/20GelUmntEIiIXkjz58/H4sWLMXbsWH3Z+PHj0bRpU8yfP58JPRGRmQqd0E+ZMgVvvvkm5s+fD1dX15KISW/79u3YsmULYmJiUKtWLVy8eBETJ05EYGBgsW79nz59OiZPnqx/rVAoEBQUBLlcDplMVuD+KpUKsffuo4GDFJ4e3kb1makKxN67Dw8PD/j6+hYpRq1WC5FIBLlcbrPJTklgv8tOv+2hz5yHg4ioZKSlpaFDhw5G5e3bt8d7771nhYiIiOxToRP6f//9F+PHjy/xZB4Apk6divfff19/63ydOnUQFxeH6OhoDBkyBP7+/gCAxMREBAQE6PdLTExE/fr182xXIpFAIpEYlYvFYrMSC93t9BCJAJGJ7f+rF4lExUpUdPvbarJTUtjvstNvW++zrcZFRGTvunXrhl27dmHq1KkG5Xv27EGXLl2sFBURkf0pdEIfGRmJc+fOoXLlyiURj4GMjAyjAbWDg4P+udbQ0FD4+/vj4MGD+gReoVDgzJkzGDVqVInHR0RERETmWbZsmf7/a9asiU8++QRHjhxBeHg4gKfP0J88eRJTpkyxVohERHan0Al9586dMXXqVFy9ehV16tSBk5OTQX23bt0sFlzXrl3xySefIDg4GLVq1cKff/6JRYsW4c033wTw9OrexIkTMW/ePFSrVk2/bF1gYCB69OhhsTiIiIiIqHgWL15s8LpcuXK4evUqrl69qi/z8vLC+vXruVwdEZGZCp3Qv/322wCAOXPmGNWJRCJoNJriR/Wf5cuXY8aMGRg9ejSSkpIQGBiIkSNHYubMmfptpk2bhidPnmDEiBFIS0tDs2bNsH//fj77SkRERGRDYmNjrR0CEdELp9AJfWku4+Th4YElS5ZgyZIleW4jEokwZ84ckycYiIiIiMi2CYIAAAYrJxERkXk44xMRERERlbrNmzejTp06kEqlkEqlqFu3Lr755htrh0VEZFcKfYWeiIiIiKg4Fi1ahBkzZmDs2LFo2rQpAODEiRN455138OjRI0yaNMnKERIR2Qcm9ERERERUqpYvX47Vq1dj8ODB+rJu3bqhVq1amDVrFhN6IiIz8ZZ7IiIiIipV8fHxeO2114zKX3vtNcTHx1shIiIi+8Qr9FaSnJwMhUKRZ72Hh0cpRkNERERUeqpWrYrt27fjgw8+MCjftm0bqlWrZqWoiIjsj1kJvUKhgEwm0/9/flxdXeHoyPME+UlOTsabI96BMjMrz21krlJ8Fv0JfH19SzEyIiIiopI3e/Zs9OvXD8eOHdM/Q3/y5EkcPHgQ27dvt3J0RET2w6zMu1y5coiPj4evry+8vLzyXVZEJBKhWrVqWLVqFVq3bm2xQF8kCoUCyswstBo0Cj4BFY3qU+If4Oi3a5CRkWGF6IiIiIhKVu/evXHmzBksXrwYu3fvBgDUqFEDf/zxBxo0aGDd4IiI7IhZCf2hQ4fg7e0NADh8+HC+26rVauzevRujRo3C9evXix/hC8wnoCL8Q0KtHQYRERFRqWvUqBG+/fZba4dBRGTXzEroW7ZsafL/81K/fn388ccfRY+KiIiIiKgAq1evxurVq3H37l0AQK1atTBz5kx07NgRAJCVlYUpU6Zg69atUKvViIyMxKpVq+Dn52fFqImILKdID7trNBrs2rUL165dAwDUrFkT3bt31z877+vri3PnzlkuSiIiIiKi51SsWBGffvopqlWrBkEQsGnTJnTv3h1//vknatWqhUmTJmHfvn3YsWMHPD09MXbsWPTq1QsnT560duhERBZR6IT+ypUr6NatGxISElC9enUAwIIFCyCXy/Hjjz+idu3aFg+SiIiIiOh5Xbt2NXj9ySefYPXq1Th9+jQqVqyIdevWISYmBm3atAEAbNiwATVq1MDp06fx6quvWiNkIiKLKvQ69G+99RZq1aqFBw8e4MKFC7hw4QLu37+PunXrYsSIESURIxERERFRvjQaDbZu3YonT54gPDwc58+fR05ODiIiIvTbhIWFITg4GKdOnbJipEREllPoK/QXL17EuXPnUK5cOX1ZuXLl8Mknn6Bx48YWDY6IiIiIKD+XLl1CeHg4srKy4O7ujl27dqFmzZq4ePEinJ2d4eXlZbC9n58fEhIS8mxPrVZDrVbrX+uWbNZqtdBqtQXGIwgCRCKR/sfSdO0KgmBWPC8SrVbLfpcxtt53W4ir0An9Sy+9hMTERNSqVcugPCkpCVWrVrVYYERERET04ujVq5fZ2+7cudPsbatXr46LFy8iPT0d33//PYYMGYKjR48WJUQAQHR0NGbPnm1UnpycjKysrAL3VyqVqFatGtzc3ODi4lLkOPKiVqshk8mgUqmQlJRk8fZtmVarRXp6OgRBgFhc6BuN7VZZ7Tdg+31XKpXWDsG8hF53ZhJ4+kdu/PjxmDVrlv7Zo9OnT2POnDlYsGBByURJRERERHbN09OzRNp1dnbWX1Rq1KgRzp49i6VLl6Jfv37Izs5GWlqawVX6xMRE+Pv759ne9OnTMXnyZP1rhUKBoKAgyOVyyGSyAuNRqVS4efMmvLy84ObmVvSO5SEjIwNyuRzu7u7w9fW1ePu2TKvVQiQSQS6X22RyV1LKar8B2+97SZy0KyyzEnovLy+DW4YEQUDfvn31ZYIgAHg6MYlGoymBMImIiIjInm3YsKFUjqPVaqFWq9GoUSM4OTnh4MGD6N27NwDgxo0buHfvHsLDw/PcXyKRQCKRGJWLxWKzEgrd7fC6H0vTtSsSiWwywSlpun6Xtb6X1X4Dtt13W4jJrIT+8OHDJR0HEREREVGhTJ8+HR07dkRwcDCUSiViYmJw5MgR/Prrr/D09MTw4cMxefJkeHt7QyaTYdy4cQgPD+cM90T0wjAroW/ZsmVJx0FEREREZcj333+P7du34969e8jOzjaou3DhglltJCUlYfDgwYiPj4enpyfq1q2LX3/9Fe3atQMALF68GGKxGL1794ZarUZkZCRWrVpl8b4QEVlLoSfFA4C0tDSsW7cO165dAwDUqlULb775Zok9G0VEREREL45ly5bhww8/xNChQ7Fnzx4MGzYMt2/fxtmzZzFmzBiz21m3bl2+9S4uLli5ciVWrlxZ3JCJiGxSoW/6P3fuHKpUqYLFixcjNTUVqampWLRoEapUqWL22VQiIiIiKrtWrVqFtWvXYvny5XB2dsa0adNw4MABjB8/Hunp6dYOj4jIbhQ6oZ80aRK6deuGu3fvYufOndi5cydiY2PRpUsXTJw4sQRCJCIiIqIXyb179/Daa68BAKRSqX7pp0GDBuG7776zZmhERHalSFfo33vvPTg6/v/d+o6Ojpg2bRrOnTtn0eCIiIiI6MXj7++P1NRUAEBwcDBOnz4NAIiNjS2RmeGJiF5UhU7oZTIZ7t27Z1R+//59eHh4WCQoIiIiInpxtWnTBnv37gUADBs2DJMmTUK7du3Qr18/9OzZ08rRERHZj0JPitevXz8MHz4cn3/+uf5WqZMnT2Lq1KkYMGCAxQMkIiIiohfL2rVrodVqAQBjxoyBj48Pfv/9d3Tr1g0jR460cnRERPaj0An9559/DpFIhMGDByM3NxcA4OTkhFGjRuHTTz+1eIBERERE9GJ58OABgoKC9K/79++P/v37QxAE3L9/H8HBwVaMjojIfhQ6oXd2dsbSpUsRHR2N27dvAwCqVKkCV1dXiwdHRERERC+e0NBQxMfHw9fX16A8NTUVoaGh0Gg0VoqMiMi+FGkdegBwdXVFnTp1LBkLEREREZUBgiBAJBIZlatUKri4uFghIiIi+1TkhL60/Pvvv3jvvffwyy+/ICMjA1WrVsWGDRvw8ssvA3j6D8LHH3+Mr776CmlpaWjatClWr16NatWqWTlyIiIiInrW5MmTAQAikQgzZswwuMNTo9HgzJkzqF+/vpWiIyKyPzad0D9+/BhNmzZF69at8csvv0Aul+PmzZsoV66cfpuFCxdi2bJl2LRpE0JDQzFjxgxERkbi6tWrPMNLREREZEP+/PNPAE8vyFy6dAnOzs76OmdnZ9SrVw/vvvuutcIjIrI7Np3QL1iwAEFBQdiwYYO+LDQ0VP//giBgyZIl+Oijj9C9e3cAwObNm+Hn54fdu3ejf//+pR4zEREREZl2+PBhAE+Xqlu6dClkMpmVIyIism+FXoe+NO3duxcvv/wy+vTpA19fXzRo0ABfffWVvj42NhYJCQmIiIjQl3l6eqJJkyY4deqUNUImIiIiogJs2LBBn8w/ePAADx48sHJERET2yaav0N+5cwerV6/G5MmT8cEHH+Ds2bMYP348nJ2dMWTIECQkJAAA/Pz8DPbz8/PT15miVquhVqv1rxUKBQBAq9Xq10TNj34iF0EABBPb/1cvCILJ9oq7/4tMq9Wy32WEPfTZlmMjIrJnWq0W8+bNwxdffAGVSgUA8PDwwJQpU/Dhhx9CLLbpa05ERDbDphN6rVaLl19+GfPnzwcANGjQAJcvX8aaNWswZMiQIrcbHR2N2bNnG5UnJycjKyurwP2VSiVCg4Mg1WRCpEw1qpdqMhEaHASlUomkpKQi7V8pOAiZmZlISkoqU/+oabVapKenQxAE9vsFZw99ViqV1g6BiOiF9OGHH2LdunX49NNP0bRpUwDAiRMnMGvWLGRlZeGTTz6xcoRERPbBphP6gIAA1KxZ06CsRo0a+OGHHwAA/v7+AIDExEQEBATot0lMTMx3htTp06frZ1kFnl6hDwoKglwuN+tZLpVKhdh799HAQQpPD2+j+sxUBWLv3YeHh4fR+qrm7n/33n1IpVL4+vrabLJTErRaLUQiEeRyOfv9grOHPnNiTSKikrFp0yZ8/fXX6Natm76sbt26qFChAkaPHs2EnojITDad0Ddt2hQ3btwwKPvnn38QEhIC4OkEef7+/jh48KA+gVcoFDhz5gxGjRqVZ7sSiQQSicSoXCwWm5VY6G6Hh0gEiExs/1+9SCQy2V5h97fVZKeksN9lp9+23mdbjYuIyN6lpqYiLCzMqDwsLAypqcZ3LxIRkWk2PVqdNGkSTp8+jfnz5+PWrVuIiYnB2rVrMWbMGABPk4GJEydi3rx52Lt3Ly5duoTBgwcjMDAQPXr0sG7wRERERGRSvXr1sGLFCqPyFStWoF69elaIiIjIPtn0FfrGjRtj165dmD59OubMmYPQ0FAsWbIEUVFR+m2mTZuGJ0+eYMSIEUhLS0OzZs2wf/9+3ipLREREZKMWLlyIzp0747fffkN4eDgA4NSpU7h//z5+/vlnK0dHRGQ/bDqhB4AuXbqgS5cuedaLRCLMmTMHc+bMKcWoCpadrUZcXJzJuri4OOTm5pZyRERERES2oWXLlvjnn3+wcuVKXL9+HQDQq1cvjB49GoGBgVaOjojIfth8Qm+PlGmpiL19Bx/OnW/yWf3MjCd4mJCInJxsK0RHREREZF337t1DUFCQycnv7t27h+DgYCtERURkf5jQl4CsjCcQOzmh5aBRqFCpilH9zYtn8cOqz6HRaKwQHREREZF1hYaGIj4+3mg1oJSUFISGhnKMRERkJib0JcjHPxD+IaFG5ckP71shGiIiIiLboFvN53kqlYrzIBERFQITeiIiIiIqFZMnTwbwdA6kGTNmwNXVVV+n0Whw5swZ/VLERERUMCb0RERERFQq/vzzTwBPr9BfunQJzs7O+jpnZ2fUq1cP7777rrXCIyKyO0zoiYiIiKhUHD58GAAwbNgwLF26FDKZzMoRERHZNyb0RERERFSqNmzYYO0QiIheCGJrB0BEREREREREhceEnoiIiIiIiMgOMaEnIiIiIiIiskNM6ImIiIiIiIjsEBN6IiIiIiIiIjvEhJ6IiIiIiIjIDjGhJyIiIiIiIrJDTOiJiIiIiIiI7JCjtQMgIiIiIiIiMkdycjIUCkWJtS+TySCXy0usfUtjQk9ENqWgP9L29keWiIhKTnR0NHbu3Inr169DKpXitddew4IFC1C9enX9NllZWZgyZQq2bt0KtVqNyMhIrFq1Cn5+flaMnIiKIjk5GaPeHgF1RkaJHUPi6orVX621m/EmE3oishnJycl4c8Q7UGZm5bmNh9QF69eusZs/skREVHKOHj2KMWPGoHHjxsjNzcUHH3yA9u3b4+rVq3BzcwMATJo0Cfv27cOOHTvg6emJsWPHolevXjh58qSVoyeiwlIoFFBnZGBK/0EI8g+wePv3E+LxxdZvoFAo7GasyYSeiGyGQqGAMjMLrQaNgk9ARaP6lPgHOPLNarv6I0tERCVn//79Bq83btwIX19fnD9/Hi1atEB6ejrWrVuHmJgYtGnTBgCwYcMG1KhRA6dPn8arr75qjbCJqJiC/ANQJSjY2mHYBCb0RGRzfAIqwj8k1NphEBGRnUlPTwcAeHt7AwDOnz+PnJwcRERE6LcJCwtDcHAwTp06ZTKhV6vVUKvV+te6x8C0Wi20Wm2BMQiCAJFIpP+xNF27giCYFc+LRKvVst924tGjRxZ5zl0QBCiVSiiVSohEIsTFxUGj0Tx9PwTBApEaH68wv1+28JkwoSciIiIiu6fVajFx4kQ0bdoUtWvXBgAkJCTA2dkZXl5eBtv6+fkhISHBZDvR0dGYPXu2UXlycjKysvJ+JExHqVSiWrVqcHNzg4uLS+E7UgC1Wg2ZTAaVSoWkpCSLt2/LtFot0tPTIQgCxOKys1iXvfU7PT0dq1esQM4zJ8aKTgS5rxzJSckABGSp1ZC6uSE1Vw33DJUF2jekFDQICgmGUqk06/dLqVRaPIbCYkJPRERERHZvzJgxuHz5Mk6cOFGsdqZPn47JkyfrXysUCgQFBUEul0MmkxW4v0qlws2bN+Hl5aV/jt+SMjIyIJfL4e7uDl9fX4u3b8u0Wi1EIhHkcrldJLaWYm/9VqlUuPPPTUzq9waC/PyL1ZYgCFAKGniIHCASiXDm0l+I/noNXHK18HV1t1DE/08lSsX9uHvw8PAw6/erJE7aFRYTeiIiIiKya2PHjsVPP/2EY8eOoWLF/5+Dxd/fH9nZ2UhLSzO4Sp+YmAh/f9OJhkQigUQiMSoXi8VmJVO623V1P5ama1ckEtlFcmdpun6Xtb7bU791vwPBFnjOXSsISMpQwdfVHWKRCPcS4p+e4IAI4hJ6pKUwv1+28HlYPwIiIiIioiIQBAFjx47Frl27cOjQIYSGGs6/0qhRIzg5OeHgwYP6shs3buDevXsIDw8v7XCJiCyOV+iJiIiIyC6NGTMGMTEx2LNnDzw8PPTPxXt6ekIqlcLT0xPDhw/H5MmT4e3tDZlMhnHjxiE8PJwz3BPRC4EJPRERERHZpdWrVwMAWrVqZVC+YcMGDB06FACwePFiiMVi9O7dG2q1GpGRkVi1alUpR0pEVDKY0JdRycnJ+S4lIZPJuM43ERER2TRznlF3cXHBypUrsXLlylKIiIiodDGhL4OSk5Px5oh3oMzMe+kVD6kL1q9dw6SeiIiIiIjIRtlVQv/pp59i+vTpmDBhApYsWQIAyMrKwpQpU7B161aD26j8/PysG6wNUygUUGZmodWgUfAJqGhUnxL/AEe+WQ2FQsGEnoiIiMjG5ObmIi4uDqISmOWbd2kS2Re7SejPnj2LL7/8EnXr1jUonzRpEvbt24cdO3bA09MTY8eORa9evXDy5EkrRWo/fAIqwj8ktOANiYiIiMgmZGdnIzk5GRMnToSTk5PF2/fx8UFMTAyTeiI7YRcJvUqlQlRUFL766ivMmzdPX56eno5169YhJiYGbdq0AfB0EpQaNWrg9OnTZXr20vyekY+Li0Nubm4pR0RERERExaXRaCAIApydneHp6WnRtjMzM5GSksK7NInsiF0k9GPGjEHnzp0RERFhkNCfP38eOTk5iIiI0JeFhYUhODgYp06dKrMJfUHPyGdmPMHDhETk5GSXcmREREREZAkSiQRubm4Wb1etVlu8TSIqOTaf0G/duhUXLlzA2bNnjeoSEhLg7OwMLy8vg3I/Pz/9OqSmqNVqgz9WuivZWq0WWq22wJgEQXj6zJIgAIKp7QWIxeKi1//XviAIZsXzvPT0dKiy1Gg16B34+Bs/I3/rr3PYuWYRNLm5JXL84tBqtVY5rrWVxX6b6nOBv1ul/N0sS58HEREREdkfm07o79+/jwkTJuDAgQNwcXGxWLvR0dGYPXu2UXlycjKysvKe+V1HqVQiNDgIUk0mRMpUo3pPB6BWWHW4CdlFqpdqMlEpOAiZmZlISkp6mvwXgi6+inIfeHnLjOqzA/wKPH5ocBCUSiWSkpIKdezi0mq1SE9PhyAIhe63PSuL/TbV54J+t0r7u6lUKkv8GERERERERWXTCf358+eRlJSEhg0b6ss0Gg2OHTuGFStW4Ndff0V2djbS0tIMrtInJibC398/z3anT5+OyZMn618rFAoEBQVBLpdDJjNOgJ+nUqkQe+8+GjhI4enhbVSfrgGuXL+BtiJnCEWoz0xV4O69+5BKpfD19S10glfc+DJTFYi9dx8eHh7w9fUt1LGLS6vVQiQSQS6Xl5nEFiib/TbV54K+u6X93bTkiUQiIiIiIkuz6YS+bdu2uHTpkkHZsGHDEBYWhvfeew9BQUFwcnLCwYMH0bt3bwDAjRs3cO/ePYSHh+fZrkQigUQiMSoXi8VmJVO6W34hEgEiU9uLnt6qW9T6/9oXiURmx2TR+J47fmkrar/tXVns9/N9LvC7W8rfzbL0WRARERGR/bHphN7DwwO1a9c2KHNzc4OPj4++fPjw4Zg8eTK8vb0hk8kwbtw4hIeHl9kJ8YiIiIiIiKhssOmE3hyLFy+GWCxG7969oVarERkZiVWrVlk7LCIiIiIiIqISZXcJ/ZEjRwxeu7i4YOXKlVi5cqV1AiIiIiIiIiKyAj4gSkRERERERGSH7O4KPRGVbdnZasTFxeVZL5PJIJfLSzEiIiIiIiLrYEJPRHZDmZaK2Nt38OHc+SZXqgAAD6kL1q9dw6SeiIiIiF54TOiJyKKSk5OhUCjyrC/OFfSsjCcQOzmh5aBRqFCpilF9SvwDHPlmNRQKBRN6IiIiInrhMaEnIotJTk7GmyPegTIzK89tdFfQfXx8inwcH/9A+IeEFnl/IiIiIqIXARN6IrIYhUIBZWYWWg0aBZ+Aikb1z15BL05CT0RERERETOiJqAT4BFTkFXQiIiIiohLGhJ6IiIiIiAAAOTk5+a4mUxxciYbI8pjQExERERERsrOzERcXh3HjxuW5mkxx+Pj4ICYmhkk9kQUxoSciIiIiImg0GuTm5sLZ2RleXl4WbTszMxMpKSlciYbIwpjQExERERGRnouLC9zc3CzerlqttnibRGWd2NoBEBEREREREVHhMaEnIiIiIiIiskNM6ImIiIiIiIjsEJ+ht1HZ2WokJyfjzp07EIlERvVc9oOIiIiIiKhsY0Jvg5Rpqbh7JxbfbN2OB/EJEATBaBsPqQvWr13DpJ6IiIiIiKiMYkJvg7IynkDs5ITarTvhlcBQ4Lkr9CnxD3Dkm9Vc9oOIiIiIiKgMY0JvwzzKeUMeUgkQcaoDIiIiIiIiMsRMkYiIiIiIiMgOMaEnIiIiIiIiskNM6ImIiIiIiIjsEBN6IiIiIiIiIjvEhJ6IiIiI7NaxY8fQtWtXBAYGQiQSYffu3Qb1giBg5syZCAgIgFQqRUREBG7evGmdYImILIwJPRERERHZrSdPnqBevXpYuXKlyfqFCxdi2bJlWLNmDc6cOQM3NzdERkYiKyurlCMlIrI8Lltnp7Kz1YiLizNZFxcXh9zc3FKOiIiIiKj0dezYER07djRZJwgClixZgo8++gjdu3cHAGzevBl+fn7YvXs3+vfvX5qhEhFZHBN6O6RMS0Xs7Tv4cO58SCQSo/rMjCd4mJCInJxsK0RHREREZBtiY2ORkJCAiIgIfZmnpyeaNGmCU6dOmUzo1Wo11Gq1/rVCoQAAaLVaaLXaAo8pCAJEIpH+x9KebdvS7YtEIojF4hJrWyQSQRAEs95HU7RabbH2t1f21m/d74AgCNAKQrHa0gqCQTsCALFYDAHFb9sUg9jNeL9t4TNhQm+HsjKeQOzkhJaDRqFCpSpG9TcvnsUPqz6HRqOxQnRE+dPdXSIIApRKJVQqlX7QwLtLiIjIkhISEgAAfn5+BuV+fn76uudFR0dj9uzZRuXJyclm3aavVCpRrVo1uLm5wcXFpQhR58/T0xNisRharRYymaxE2g4NDbV421lZWXjy5AmUSiWSkpKK1IZWq0V6ejoEQYBYXHaeHLa3fiuVSgSFBEMpaJCUoSpWW1oISM/OgiACxBBB4+yIl2qEIUOEYrdtilLQPI3dzO+pUqm0eAyFxYTejvn4B8I/JNSoPPnhfStEQ1SwZ+8ucXFxQWhwEGLv3Yfw3xlW3l1CRETWNn36dEyePFn/WqFQICgoCHK53KwkV6VS4ebNm/Dy8oKbm5vF40tJSdEn9D4+PhZt+9GjR/j777+h1WpRvnx5i7b95MkTpKWlwcPDA76+vkVqQ6vVQiQSQS6X20Viayn21m+VSoX7cffgIXKAr6t7sdrSCgJEAiCXukMsEsEhOxf/XLsOVwHFbtsUlSj1aexmfk9L4qRdYdl0Qh8dHY2dO3fi+vXrkEqleO2117BgwQJUr15dv01WVhamTJmCrVu3Qq1WIzIyEqtWrTI6E0tE1mdwd0lIZUg1mWjgIAX+u0LPu0uIiMiS/P39AQCJiYkICAjQlycmJqJ+/fom95FIJCYfaRSLxWYlU7rbdXU/lvZs25ZuX3ebcUm1rbuduThJqW5/e0hsLcme+q37HRCJRBBb4NENXTtikQgi/HeCA5Zp29SxCvM9tYXPw/oR5OPo0aMYM2YMTp8+jQMHDiAnJwft27fHkydP9NtMmjQJP/74I3bs2IGjR4/i4cOH6NWrlxWjJqKCPL27pBK85H7wD6kE/5BQ+IeEwsvX39qhERHRCyQ0NBT+/v44ePCgvkyhUODMmTMIDw+3YmRERJZh01fo9+/fb/B648aN8PX1xfnz59GiRQukp6dj3bp1iImJQZs2bQAAGzZsQI0aNXD69Gm8+uqr1gibiIiIiEqJSqXCrVu39K9jY2Nx8eJFeHt7Izg4GBMnTsS8efNQrVo1hIaGYsaMGQgMDESPHj1KJT61Wm3R+WHUajUkEgnUajWePHkCR0dHk3cUEFHZYNMJ/fPS09MBAN7e3gCA8+fPIycnx2Dm0rCwMAQHB+PUqVNM6ImIiIhecOfOnUPr1q31r3XPvw8ZMgQbN27EtGnT8OTJE4wYMQJpaWlo1qwZ9u/fXyrPvqrVatz55yacHRws1qZWK8C3ShXExiciMy0d2RoNKr9UjUk9URllNwm9VqvFxIkT0bRpU9SuXRvA05lLnZ2d4eXlZbBtfjOXApZbjgSCAAimtv9vBkqL1AsAnt/Gku2bqBcE5ORk4+7du3k+PyWTySw+WQpgf8tyWMqL0u/C/W4IJr7jxf/uFndJnGfZ++dBRFQWtGrVKt/nvUUiEebMmYM5c+aUYlRP5ebmwtnBAeN694Oft2UmsMvJyUG2kwOcW2qQqlRg+Q/bkJuby4SeqIyym4R+zJgxuHz5Mk6cOFHstiyxHElocBCkmkyIlKlG9Z4OQK2w6nATsotcX7N6dbggFyJVKgBRofcvTr2Qngw3Fxds3PIdnJycTL4HUmdnjB87Gp6enibri8reluWwlBel34X63VClQpSp/O/rLTKuL8J3V6rJRGhwULGWxHm+P0RERMXl5+2Dir6WmbBZnZ2NdE0OPB2c8hynEVHZYRcJ/dixY/HTTz/h2LFjqFixor7c398f2dnZSEtLM7hKn5iYqJ/V1BRLLEcSe+8+GjhI4enhbVSfrgGuXL+BtiJnCEWsv3rjBrLgCA93b/0M4JZsP7/6RxlXcO3mLTTsPRQVQiob1ackPMDRb7+Eg4NDkZcdyYs5y3I8evRIf1eFKSV190BJsrflSPJSqN8Nd29AwNP//vcdL+53NzNVgdh794u1JM6zbGEpEiIiImuy1BwAmZmZUKvViIuL05fJZDLI5fJitw08vTCX3/iwuCwZK5El2XRCLwgCxo0bh127duHIkSMIDTVcc71Ro0ZwcnLCwYMH0bt3bwDAjRs3cO/evXxnLrXUciQQiQCRqe1FT2/VtUi9qW0s2X7e9T7+gfCvZJzQo5DLORRWfstyJCcnY/jIUVBm5n0nhYfUBevXrrG7P7r2tBxJXgr3uyEy8R0v5nfXwt9Ne/4siIiIisuScwBotVpoNBp8OnsuJBJnAIDE1RWrv1pb7DFbcnIyBg4ciJSUlGLHmRcfHx/ExMTY3fiSXnw2ndCPGTMGMTEx2LNnDzw8PPTPxXt6ekIqlcLT0xPDhw/H5MmT4e3tDZlMhnHjxiE8PJwT4tmx9PR0qFSqp89iPycuLg6PlSpEDBsHn4CKRvUp8Q/wv3VLcOnSJYSEhJhsn2dYiYiIiApmyTkANBoNcnNzUbt2HbhKpbifEI8vtn4DhUJR7HGZQqFASkoKJBIJpFJpsdoyJTMzEykpKRaJlcjSbDqhX716NYCnk508a8OGDRg6dCgAYPHixRCLxejduzfUajUiIyOxatWqUo6ULOXRo0dYtmIVrt66bXKCm8yMJ3iYkAhZeV/4h4Qa1SvTUhF7+w4+nDs/z8lh7PUKPhEREZE1WGIOAE2uBjm5OahcMQhurq4WisyQVCqFm5tbibT97ITaRLbEphP6/GYs1XFxccHKlSuxcuXKUoiISppCoUBmdjZavjESPgFBRvU3L57FD6s+h0ajMbl/VsYTiJ2c0HLQKFSoVMWoPiX+AY58s5pnWImIiIiIyO7ZdEJPZZePf0WTV+CTH943c/9Ak/sTERERERG9KDjjExEREREREZEdYkJPREREREREZIeY0BMRERERERHZISb0RERERERERHaICT0RERERERGRHeIs92RxycnJUCgUedbLZDIuGUdERERERFRMTOjJopKTk/HmiHegzMzKcxsPqQvWr13DpJ6IiIiIiKgYmNCTRSkUCigzs9Bq0Cj4BFQ0qk+Jf4Aj36yGQqFgQk9ERERERFQMTOipRPgEVIR/SKi1wyAiIiIiInphcVI8IiIiIiIiIjvEhJ6IiIiIiIjIDvGWeyqS7Gw14uLijMrj4uKQm5trhYiotOS3ioEtfP55fTd1uMoCEREREb0omNBToSnTUhF7+w4+nDsfEonEoC4z4wkeJiQiJyfbStFRSSpoFQNrf/75fTd1uMoCEREREb0omNBToWVlPIHYyQktB41ChUpVDOpuXjyLH1Z9Do1GY6XoqCQVtIqBtT///L6bAFdZICIisiatVkBmZgYAICMzE2p1dr531T1PEAQolUqoVCqIRCJ9uS3cIVhWmbpzMy4uDmp1NjIyM/EkI6PIbTs6OsLJyam4Ib7wmNBTkfn4BxrNZJ/88L6VoqHSlNcqBrby+Zv6bhIREZH1aAUtMjMzcfnyZYjFYvybnITY2DsYN25cnnfVPU8kEqFatWq4efMmBEHQl6vVaty/fx+enp4lFT6ZkJycjIEDByIlJcWgXK1WI1upwuXLl/A4MbHI7Ts5OaN+gwbFDfOFx4SeqJDye4Yc4DPaRERERM8TBAGCoIVIJIaToxMcHR3h4OAAmUwGqVRqVhsikQhubm7w8vIySOgfP36M3NxcXqUvZQqFAikpKZBIJAafYWZmJh5nZD69wu5YtCvsGq0GOTnZ/EzNwISeqBAKeoYc4DPaRERERHlxcBDDwdEBDg4OEIvFkEqlcHNzM2tfkUgEFxcXuLm5GST0mZmZJRUumcHUZ5guFsPBwQEOjg5FazQX0Gq1FojuxceEnqgQCnqGnM9oExERUWnK1WgsltBmZWVBq9UiKysLT548QWZmpkHiTES2hwk9URHk9Qw5ERERUWlJV6mgUqkgTkhEulhc7PZyczWQy7zwJDUNaoUS2Tk50Go0vFJKZMOY0FOpy2+d8Li4uBKfIb2gdcqzs7Ph7Oxsso6zqBIREZGtyFRnQSpxwbhe/RBggTsDc3JyoFKp4O7uDicnJ1y+fQubfv6RV+mJbBgTeipVBa0TnpWZgXJe5UpsHfOCjp+drcb9u3cRUrkKHB2Nfz2svc46ERER0fN8vb1R0dev2O1kZ2dDIXGBTCaDs7MzElIeWSA6IipJTOipVBW0Tviti2dx48i+ErtKX9Dxb148i7hVn6PZwBF51ltznXUiIiIiIiIdJvRkFXmtE5788J6Vj3/frHoiIiIiKp7CTugnEon0E/Y9+xjA85P56Wi1WogtMLdAZmYm1GrjRzbze0yzuLgMMqDVCsjKzECWWIQnEEP83+cvCAKysjLxJCOjyG07OjpCUkKfXWljQk9ERERERKWqKBP6icVieLq6IfHefYOJ+p6fzA94erJAoVKhnEwGkUhUrFi1Wi00Gg0+nT0XEsnTJFCdnY3YuDhUDQ01+ZhmcUlcXbH6q7VlNqnXClpkZmbi8pUr0Li6IC4rGyIA//xzA2q1Gv/88w8y0xVFbt/JyRkNGzZ8IZJ6JvRERERERFSqijyhn7sr0LSVQdHzk/kB0E/oN7Zn32JPGKjRaJCbm4vatevAVSoFAJz++yI+WbsK418fgJcqWXblo/sJ8fhi6zdlehlkQRAgCFqIRGI4ih3g5OgEEQAHB0eIRCI4OjrCydGpSG1rtBrk5GQjNzeXCT0RGStoFn1bv4UqOTkZCoXpM56c5Z+IiIgsqTAT+gkA1E4OkLh64Nlr7s9P5gdAP6GfJSYM1ORqkJObg8oVg+Dm6goAiIt/CACo6OePKkHBxWqf8ubgIIbgIIaDo8N/Cb0YgAhi8dOyIsnFC7UUIxN6IgsqaBZ9APCQumD92jU2mdQnJyfjzRHvQJmZZbKes/wTEREREdmOFyahX7lyJT777DMkJCSgXr16WL58OV555RVrh0VlTEGz6KfEP8CRb1bb7C1UCoUCyswstBo0Cj4BFY3qOcs/ERHZK44ViehF9EIk9Nu2bcPkyZOxZs0aNGnSBEuWLEFkZCRu3LgBX19fa4dHZVBes+TbC5+Aipzln4iIXhgcKxLRi6r46zjYgEWLFuHtt9/GsGHDULNmTaxZswaurq5Yv369tUMjMqJ7xv727dv6nzt37iA+Ph537txBcnKytUMkIiJ6oXCsSEQvKru/Qp+dnY3z589j+vTp+jKxWIyIiAicOnXKipERGcvrGXuRSITQ4CDE3rsPdxeJzT5jT0REZG84ViSiF5ndJ/SPHj2CRqOBn5/h7JV+fn64fv26yX3UajXUarX+dXp6OgAgLS3NrBkPFQoFNJpcPLx9A5kqpVF90r1YQBDwMPYWBBMzgptbn/zwAVQaMfDc2pmWar8k6ovbdvK9u9Dk5uLh3dsQTDynbct9N6f+3vXLEDs6okaLSPj4BxrUyRwB5+BHuPi/PTh16hSCgoKM9i9p9+/fR7ZaXeLf7YextyDk5ECqyUKmw7/673hJv/+PEx9Co8mFQqFAWlpage+HbrZ/QRAK3JaIiGxTYceKlhgnarVaqFQqAIBGq8WdBw+gfPKkON3Qy8nNhVbihAR1Du4nJgIQEPvwX2RkmZ7QtrBtZ2VmwuVxKpwcHS3avqXaFnm4QlBm5Ns2AIvGrluHPtfJES4uLgCAa7duQisIuHzzOpQZlvlsdR4mJSE9XYFDhw7hypUrEAQBGo0GDg4OED2XF1hTYmIiVCqVwfcdeHoSLVejKdb3/tnP1LmcTP+ZW+Jz1Wq10Gq1cPRwh/S/z1Pn36Qk5Go0djVWFAl2PlJ9+PAhKlSogN9//x3h4eH68mnTpuHo0aM4c+aM0T6zZs3C7NmzSzNMIrJj9+/fR8WKxpMEEhGR7SvsWJHjRCIqLGuOFe3+Cn358uXh4OCAxMREg/LExET4+/ub3Gf69On4v/buPCjK844D+Hc5lvsUuSyBgBdtylEYDVALcXQ8Oia0iTLAGG09YpUGtHGoF6jEaFMbjxZIYqLUBoUxojHWGhsydhRQKmFNKLicSmxBY0waxBiuX//I8NblUEBgWfb7mdk/3vd9nmd/v3fx3d+zvvvsmjVrlO2Ojg7cvn0bY8aMGTGfen399dfw8vLCZ599Bnt7e32HM2yYt/HkbQg5iwiamprg6en58MZERDQi9bdWHOl1oiG8fw4VY83dWPMGRn7uI6FWNPgJvVqtRkhICPLz8xEdHQ3guwtvfn4+EhISeuxjYWHR7TfCHR0dhzjSgbG3tx+Rf7xDjXkbj5Ges4ODg75DICKiR9DfWtFQ6sSR/v45lIw1d2PNGxjZueu7VjT4CT0ArFmzBosWLUJoaCimTJmC3bt3o7m5Gb/4xS/0HRoRERER6RlrRSIarUbFhD4mJgaff/45UlJS0NjYiKCgIJw+fbrb4idEREREZHxYKxLRaDUqJvQAkJCQ0Ost9obIwsICqamp3W75Gu2Yt/HkbYw5ExGR/oyWWtGY3z+NNXdjzRsw7tz7yuBXuSciIiIiIiIyRib6DoCIiIiIiIiI+o8TeiIiIiIiIiIDxAk9ERERERERkQHihF6P0tPT4ePjA0tLS0ydOhXFxcW9tt23bx+mTZsGJycnODk5YcaMGQ9sP5L1J+/75eTkQKVSKb8ha0j6m/NXX32FVatWwcPDAxYWFpg4cSJOnTo1TNEOnv7mvXv3bkyaNAlWVlbw8vLC6tWrce/evWGKloiIaGQw1hoRMM46ETDeWhFgvfjIhPQiJydH1Gq17N+/X/71r3/JsmXLxNHRUW7cuNFj+7i4OElPT5fS0lKpqKiQxYsXi4ODg1y/fn2YI380/c27U11dnYwbN06mTZsmzzzzzPAEO0j6m/O3334roaGhMnfuXDl//rzU1dXJ2bNnRaPRDHPkj6a/eWdnZ4uFhYVkZ2dLXV2dfPDBB+Lh4SGrV68e5siJiIj0x1hrRBHjrBNFjLdWFGG9OBg4odeTKVOmyKpVq5Tt9vZ28fT0lO3bt/epf1tbm9jZ2cmf//znoQpxSAwk77a2NgkPD5e33npLFi1aZHAX6v7mnJmZKb6+vtLS0jJcIQ6J/ua9atUqmT59us6+NWvWSERExJDGSURENJIYa40oYpx1oojx1ooirBcHA2+514OWlhaUlJRgxowZyj4TExPMmDEDRUVFfRrj7t27aG1thbOz81CFOegGmvfWrVvh6uqKJUuWDEeYg2ogOZ84cQJhYWFYtWoV3Nzc8MQTT+CVV15Be3v7cIX9yAaSd3h4OEpKSpTbrGpra3Hq1CnMnTt3WGImIiLSN2OtEQHjrBMB460VAdaLg8VM3wEYo1u3bqG9vR1ubm46+93c3HDlypU+jZGcnAxPT0+dfwAj3UDyPn/+PN5++21oNJphiHDwDSTn2tpafPTRR4iPj8epU6dQXV2NlStXorW1FampqcMR9iMbSN5xcXG4desWfvzjH0NE0NbWhhUrVmD9+vXDETIREZHeGWuNCBhnnQgYb60IsF4cLPwfegO0Y8cO5OTk4NixY7C0tNR3OEOmqakJCxcuxL59++Di4qLvcIZNR0cHXF1d8eabbyIkJAQxMTHYsGEDXn/9dX2HNqTOnj2LV155BRkZGfj444+Rl5eHv/71r0hLS9N3aERERAbBWGpEwHjrRMB4a0WA9WJP+D/0euDi4gJTU1PcuHFDZ/+NGzfg7u7+wL47d+7Ejh078OGHHyIgIGAowxx0/c27pqYGV69exbx585R9HR0dAAAzMzNotVr4+fkNbdCPaCCvtYeHB8zNzWFqaqrs8/f3R2NjI1paWqBWq4c05sEwkLw3bdqEhQsXYunSpQCAH/7wh2hubsby5cuxYcMGmJjw80ciIhrdjLVGBIyzTgSMt1YEWC8OFuPLeARQq9UICQlBfn6+sq+jowP5+fkICwvrtd+rr76KtLQ0nD59GqGhocMR6qDqb96TJ0/Gp59+Co1GozyefvppPPXUU9BoNPDy8hrO8AdkIK91REQEqqurlTclAKisrISHh4fBXKAHkvfdu3e7XYQ736hEZOiCJSIiGiGMtUYEjLNOBIy3VgRYLw4a/a7JZ7xycnLEwsJCsrKypLy8XJYvXy6Ojo7S2NgoIiILFy6U3/72t0r7HTt2iFqtlnfffVcaGhqUR1NTk75SGJD+5t2VIa5e2t+c6+vrxc7OThISEkSr1crJkyfF1dVVXn75ZX2lMCD9zTs1NVXs7Ozk8OHDUltbK2fOnBE/Pz9ZsGCBvlIgIiIadsZaI4oYZ50oYry1ogjrxcHAW+71JCYmBp9//jlSUlLQ2NiIoKAgnD59WlkUor6+XufTp8zMTLS0tOC5557TGSc1NRWbN28eztAfSX/zHg36m7OXlxc++OADrF69GgEBARg3bhwSExORnJysrxQGpL95b9y4ESqVChs3bsS///1vjB07FvPmzcO2bdv0lQIREdGwM9YaETDOOhEw3loRYL04GFQixnpvAhEREREREZHhGn0fcREREREREREZAU7oiYiIiIiIiAwQJ/REREREREREBogTeiIiIiIiIiIDxAk9ERERERERkQHihJ6IiIiIiIjIAHFCT0RERERERGSAOKEnIiIiIiIiMkCc0JPCx8cHu3fvVrZVKhWOHz+ut3gGU1RUFJKSkob0Oa5evQqVSgWNRjOkz9ObwXi9Nm/ejKCgoAe2Wbx4MaKjo5Xtrue2698RERERGT7WiY+GdeJ3WCcOPjN9B0AjV0NDA5ycnPrUdvPmzTh+/LjeLlIPk5eXB3Nzc32HMSrs2bMHItLr8X/+85+wsbFRtlUqFY4dO6ZzcSciIiLDxjqResI6cfhxQj/KtLS0QK1WD8pY7u7ugzLOUOprvs7OzsMQzdBobW0dUW8yDg4ODzw+duzYYYqEiIiI+oN1Ys9YJw4e1onDj7fcj2BRUVFISEhAQkICHBwc4OLigk2bNul86uXj44O0tDQ8//zzsLe3x/LlywEA58+fx7Rp02BlZQUvLy+8+OKLaG5uVvrdvHkT8+bNg5WVFR5//HFkZ2d3e/6ut+Zcv34dsbGxcHZ2ho2NDUJDQ3Hx4kVkZWVhy5YtuHz5MlQqFVQqFbKysnrM6ezZs5gyZQpsbGzg6OiIiIgIXLt2DUD3W3QAICkpCVFRUd3OSVJSElxcXDBr1izExcUhJiZGp19raytcXFxw8OBBpV/n7T7r16/H1KlTu8UWGBiIrVu3KttvvfUW/P39YWlpicmTJyMjI0OnfXFxMYKDg2FpaYnQ0FCUlpb2mPP9Ol+v2NhY2NjYYNy4cUhPT9dpo1KpkJmZiaeffho2NjbYtm0bACAzMxN+fn5Qq9WYNGkS/vKXv3Qbv6GhAXPmzIGVlRV8fX3x7rvv6hxPTk7GxIkTYW1tDV9fX2zatAmtra3dxnnjjTfg5eUFa2trLFiwAP/973+VYz29Tl1z7LyVysfHBwDws5/9DCqVCj4+Prh69SpMTExw6dIlnX67d++Gt7c3Ojo6eh2biIiIvsM6kXUi60QCAAiNWJGRkWJrayuJiYly5coVeeedd8Ta2lrefPNNpY23t7fY29vLzp07pbq6WnnY2NjIrl27pLKyUgoKCiQ4OFgWL16s9JszZ44EBgZKUVGRXLp0ScLDw8XKykp27dqltAEgx44dExGRpqYm8fX1lWnTpsm5c+ekqqpKcnNzpbCwUO7evSu/+c1v5Ac/+IE0NDRIQ0OD3L17t1s+ra2t4uDgIC+99JJUV1dLeXm5ZGVlybVr10REZNGiRfLMM8/o9ElMTJTIyMhu52Tt2rVy5coVuXLlipw8eVKsrKykqalJaff++++LlZWVfP3110q/xMREEREpKysTAFJdXa2079xXVVUlIiLvvPOOeHh4yNGjR6W2tlaOHj0qzs7OkpWVpZyPsWPHSlxcnJSVlcn7778vvr6+AkBKS0t7fU29vb3Fzs5Otm/fLlqtVvbu3SumpqZy5swZnfPu6uoq+/fvl5qaGrl27Zrk5eWJubm5pKeni1arlT/84Q9iamoqH330kU6/MWPGyL59+0Sr1crGjRvF1NRUysvLlTZpaWlSUFAgdXV1cuLECXFzc5Pf/e53yvHU1FSxsbGR6dOnS2lpqfzjH/+Q8ePHS1xcnNKm6+t0/7ntzLHz7+jmzZsCQA4cOCANDQ1y8+ZNERGZOXOmrFy5UufcBAQESEpKSq/njoiIiP6PdSLrRNaJJCLCCf0IFhkZKf7+/tLR0aHsS05OFn9/f2Xb29tboqOjdfotWbJEli9frrPv3LlzYmJiIt98841otVoBIMXFxcrxiooKAdDrhfqNN94QOzs7+eKLL3qMNTU1VQIDAx+YzxdffCEA5OzZsz0e7+uFOjg4WKdNa2uruLi4yMGDB5V9sbGxEhMTo9Pv/otJYGCgbN26Vdlet26dTJ06Vdn28/OTQ4cO6TxPWlqahIWFich352PMmDHyzTffKMczMzP7dKGePXu2zr6YmBiZM2eOsg1AkpKSdNqEh4fLsmXLdPbNnz9f5s6dq9NvxYoVOm2mTp0qv/rVr3qN5/e//72EhIQo26mpqWJqairXr19X9v3tb38TExMTaWhoEJH+Xag74+r8O+qUm5srTk5Ocu/ePRERKSkpEZVKJXV1db3GSkRERP/HOpF1YifWicaNt9yPcE8++SRUKpWyHRYWhqqqKrS3tyv7QkNDdfpcvnwZWVlZsLW1VR6zZs1CR0cH6urqUFFRATMzM4SEhCh9Jk+eDEdHx17j0Gg0CA4OfqTvGDk7O2Px4sWYNWsW5s2bhz179qChoaHf49wfNwCYmZlhwYIFyu1gzc3NeO+99xAfH9/rGPHx8Th06BAAQERw+PBhpX1zczNqamqwZMkSnXP48ssvo6amBgBQUVGBgIAAWFpaKmOGhYX1Kf6u7cLCwlBRUaGzr+trWlFRgYiICJ19ERER3fo9bOzc3FxERETA3d0dtra22LhxI+rr63X6PPbYYxg3bpzOGB0dHdBqtX3Kry+io6NhamqKY8eOAQCysrLw1FNPKbdeERER0cOxTuyOdeJ3WCcaD07oR4H7V4oEgDt37uCFF16ARqNRHpcvX0ZVVRX8/PwG9BxWVlaDESoOHDiAoqIihIeHIzc3FxMnTsSFCxcAACYmJt1Wxezpeztd8wW+u/Dm5+fj5s2bOH78OKysrDB79uxe44iNjYVWq8XHH3+MwsJCfPbZZ8r3q+7cuQMA2Ldvn845LCsrU2Idaj3l+KiKiooQHx+PuXPn4uTJkygtLcWGDRvQ0tIy6M/1MGq1Gs8//zwOHDiAlpYWHDp0CL/85S+HPQ4iIqLRjnUi68S+YJ1ouDihH+EuXryos33hwgVMmDABpqamvfb50Y9+hPLycowfP77bQ61WY/LkyWhra0NJSYnSR6vV4quvvup1zICAAGg0Gty+fbvH42q1WufT4AcJDg7GunXrUFhYiCeeeEL5BHTs2LHdPont68+bhIeHw8vLC7m5ucjOzsb8+fMfuOLn9773PURGRiI7OxvZ2dmYOXMmXF1dAQBubm7w9PREbW1tt/P3+OOPAwD8/f3xySef4N69e8qYfb2Id2134cIF+Pv7P7CPv78/CgoKdPYVFBTg+9//fp/HLiwshLe3NzZs2IDQ0FBMmDBBWWjmfvX19fjPf/6jM4aJiQkmTZr08OR6YG5u3uPfxtKlS/Hhhx8iIyMDbW1t+PnPfz6g8YmIiIwV60RNn8ZknfjwsVknGi5O6Ee4+vp6rFmzBlqtFocPH8Yf//hHJCYmPrBPcnIyCgsLkZCQAI1Gg6qqKrz33ntISEgAAEyaNAmzZ8/GCy+8gIsXL6KkpARLly594KersbGxcHd3R3R0NAoKClBbW4ujR4+iqKgIwHerVNbV1UGj0eDWrVv49ttvu41RV1eHdevWoaioCNeuXcOZM2dQVVWlXEimT5+OS5cu4eDBg6iqqkJqairKysr6fK7i4uLw+uuv4+9///sDb6PqFB8fj5ycHBw5cqRb+y1btmD79u3Yu3cvKisr8emnn+LAgQN47bXXlOdSqVRYtmwZysvLcerUKezcubNPcRYUFODVV19FZWUl0tPTceTIkYe+pmvXrkVWVhYyMzNRVVWF1157DXl5eXjppZd02h05cgT79+9HZWUlUlNTUVxcrLzuEyZMQH19PXJyclBTU4O9e/cqtzLdz9LSEosWLcLly5dx7tw5vPjii1iwYMGAf57Gx8cH+fn5aGxsxJdffqns9/f3x5NPPonk5GTExsYO2qf7RERExoJ1IutEgHWi0dPvV/jpQSIjI2XlypWyYsUKsbe3FycnJ1m/fr3O4iddF5boVFxcLDNnzhRbW1uxsbGRgIAA2bZtm3K8oaFBfvrTn4qFhYU89thjcvDgwYcuUnH16lV59tlnxd7eXqytrSU0NFQuXrwoIiL37t2TZ599VhwdHZXVKrtqbGyU6Oho8fDwELVaLd7e3pKSkiLt7e1Km5SUFHFzcxMHBwdZvXq1JCQkdFvs5P6FNe5XXl4uAMTb21vnHPXW78svvxQLCwuxtrbWWfm0U3Z2tgQFBYlarRYnJyf5yU9+Inl5ecrxoqIiCQwMFLVaLUFBQXL06NE+LXayZcsWmT9/vlhbW4u7u7vs2bNHp03X894pIyNDfH19xdzcXCZOnKizuEtnv/T0dJk5c6ZYWFiIj4+P5Obm6rRZu3atjBkzRmxtbSUmJkZ27dolDg4OyvHORWsyMjLE09NTLC0t5bnnnpPbt28rbfq72MmJEydk/PjxYmZmJt7e3jrxvP32290W3iEiIqKHY53IOvF+rBONl0qky5dRaMSIiopCUFCQ8luNZPh8fHyQlJSk/NapsUtLS8ORI0fwySef6DsUIiIig8I6cfRhnaiLdWLf8JZ7Ihp2d+7cQVlZGf70pz/h17/+tb7DISIiIqIRgnVi/3BCT0TDLiEhASEhIYiKiuKqpURERESkYJ3YP7zlnoiIiIiIiMgA8X/oiYiIiIiIiAwQJ/REREREREREBogTeiIiIiIiIiIDxAk9ERERERERkQHihJ6IiIiIiIjIAHFCT0RERERERGSAOKEnIiIiIiIiMkCc0BMREREREREZIE7oiYiIiIiIiAzQ/wAidPhXlJTigAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测概率统计信息:\n",
      "  平均概率: 0.3846,概率标准差: 0.2693,最小概率: 0.1242,最大概率: 0.8892\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "def plot_prob(y_prob_train, y_proba_val, y_val_tag, y_prob_test=None):\n",
    "    # 绘制预测概率分布图\n",
    "    plt.figure(figsize=(10, 4))\n",
    "\n",
    "    # 子图1: 训练集概率分布直方图\n",
    "    plt.subplot(1, 3, 1)\n",
    "    plt.hist(y_prob_train\n",
    "    , bins=30, alpha=0.7, color='skyblue', edgecolor='black')\n",
    "    plt.xlabel('predict survived probability')\n",
    "    plt.ylabel('obj num')\n",
    "    plt.title('train data predict probability histogram')\n",
    "    plt.grid(True, alpha=0.3)\n",
    "\n",
    "    # 子图2: 测试集概率分布直方图\n",
    "    if not pd.isna(y_prob_test):\n",
    "        plt.subplot(1, 3, 2)\n",
    "        plt.hist(y_prob_test\n",
    "        , bins=30, alpha=0.7, color='skyblue', edgecolor='black')\n",
    "        plt.xlabel('predict survived probability')\n",
    "        plt.ylabel('obj num')\n",
    "        plt.title('test data predict probability histogram')\n",
    "        plt.grid(True, alpha=0.3)\n",
    "\n",
    "    # 子图3: 按实际标签分组的概率分布\n",
    "    plt.subplot(1, 3, 3)\n",
    "    # 实际未幸存的样本（标签为0）\n",
    "    survived_0_probs = y_proba_val[y_val_tag == 0]\n",
    "    # 实际幸存的样本（标签为1）\n",
    "    survived_1_probs = y_proba_val[y_val_tag == 1]\n",
    "\n",
    "    plt.hist(survived_0_probs, bins=10, alpha=0.7, label='NotSurvived (label=0)', color='black', edgecolor='black')\n",
    "    plt.hist(survived_1_probs, bins=10, alpha=0.7, label='Survived (label=1)', color='pink', edgecolor='black')\n",
    "    plt.xlabel('predict survived probability')\n",
    "    plt.ylabel('total obj num')\n",
    "    plt.title(' real val label with predict probability')\n",
    "    plt.legend()\n",
    "    plt.grid(True, alpha=0.3)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    # 统计信息\n",
    "    print(f\"预测概率统计信息:\")\n",
    "    print(f\"  平均概率: {np.mean(y_proba_val):.4f},概率标准差: {np.std(y_proba_val):.4f},最小概率: {np.min(y_proba_val):.4f},最大概率: {np.max(y_proba_val):.4f}\")\n",
    "    \n",
    "plot_prob(y_prob_c1_male_train[:, 1],y_prob_c1_male_val,val_male_tag)\n",
    "plot_prob(y_prob_c1_female_train[:, 1],y_prob_c1_female_val,val_female_tag)\n",
    "plot_prob(y_prob_c1_all_train[:, 1],y_prob_c1_all_val,val_tag)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5790f11",
   "metadata": {},
   "source": [
    "# 进行错误分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "17892c2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VrNtbW4HWBgAAMI2KBAAAdqO1AQAATCORAAAAprH8EwAAoCoqEgAA2I3WBgAAMMtwcCJBawMAAJhGRQIAALs5uCJBIgEAgN3Y2RIAAKAqKhIAANiN1gYAADDNwYkErQ0AAGAaFQkAAGxmGM6tSJBIAABgNwe3NkgkAACwm4MTCeZIAAAA06hIAABgMye/a4NEAgAAuzk4kaC1AQAATKMiAQCA3Zz7qg0SCQAA7ObkORK0NgAAgGlUJAAAsJuDKxIkEgAA2M3BcyRobQAAANOoSAAAYDMnT7YkkQAAwG60NgAAgFmG17DkqK19+/ZpyJAhatmypcLDw9WtWzd99NFH/xeXYWjKlCmKi4tTeHi4UlJStGPHjlo9g0QCAAAHOnDggC644AKFhobqX//6lz7//HPNnj1bzZs3910za9YszZ07Vzk5Odq0aZMiIiKUmpqqo0eP1vg5tDYAALBbAFobM2fOVJs2bbRo0SLfWEJCgu/XhmEoOztb99xzjwYOHChJWrJkiWJiYrRq1SrdcMMNNXoOFQkAAGxmeK05amP16tXq1auXrr32WkVHR6tHjx568sknfed3796tgoICpaSk+MaioqKUlJSkvLy8Gj+HRAIAgHrC4/GotLTU7/B4PNVeu2vXLi1YsEAdOnTQunXrdNttt2ns2LFavHixJKmgoECSFBMT4/e5mJgY37maIJEAAMBuXmuOrKwsRUVF+R1ZWVnVP9LrVc+ePfXggw+qR48eGjlypEaMGKGcnBxLvxqJBAAANrOqtZGZmalDhw75HZmZmdU+My4uTl26dPEb69y5s/bs2SNJio2NlSQVFhb6XVNYWOg7VxMkEgAA1BNut1uRkZF+h9vtrvbaCy64QNu3b/cb++qrr3TGGWdI+mXiZWxsrHJzc33nS0tLtWnTJiUnJ9c4JlZtAABgtwCs2pgwYYLOP/98Pfjgg7ruuuu0efNmLVy4UAsXLpQkuVwujR8/Xg888IA6dOighIQETZ48WfHx8UpLS6vxc0gkAACwWW1XXFjh3HPP1auvvqrMzEzdd999SkhIUHZ2tm688UbfNXfddZfKyso0cuRIHTx4UBdeeKHWrl2rRo0a1fg5LsMwHLcBeEXJrkCHAASlyDZ9Ax0CEHR+/vlb259RdFkfS+4TnbvekvtYiTkSAADANFobAADYLBCtjbpCIgEAgN0MV6AjsA2tDQAAYBoVCQAAbEZrAwAAmGZ4aW0AAABUQUUCAACb0doAAACmGazaAAAAqIqKBAAANqO1AQAATHPyqg0SCQAAbOa812P+H+ZIAAAA06hIAABgM1obAADANCcnErQ2AACAaVQkAACwmZMnW5JIAABgM1obAAAA1aAiAQCAzZz8ro0aJRKrV6+u8Q2vuuoq08EAAOBEv/ststPS0mp0M5fLpcrKylOJBwAA1CM1SiS8XgenUgAA2Mz7e29tAAAA8373cyR+q6ysTOvXr9eePXtUXl7ud27s2LGWBAYAgFM4eflnrROJjz/+WFdeeaWOHDmisrIytWjRQiUlJWrcuLGio6NJJAAA+B2p9T4SEyZM0IABA3TgwAGFh4dr48aN+vbbb5WYmKiHH37YjhgBAKjXDMOaIxjVOpHYtm2b7rjjDjVo0EAhISHyeDxq06aNZs2apX/84x92xAgAQL1meF2WHMGo1olEaGioGjT45WPR0dHas2ePJCkqKkp79+61NjoAABDUaj1HokePHtqyZYs6dOigPn36aMqUKSopKdFzzz2ns846y44YAQCo15y8/LPWFYkHH3xQcXFxkqTp06erefPmuu2221RcXKyFCxdaHiAAAPWdYbgsOYJRrSsSvXr18v06Ojpaa9eutTQgAABQf7AhFQAANgvWFRdWqHUikZCQIJfr+OWVXbt2nVJAAAA4jZPnSNQ6kRg/frzfzxUVFfr444+1du1a3XnnnVbFBQAA6oFaJxLjxo2rdnzevHn66KOPTjkgAACcJlgnSlqh1qs2jueKK67QihUrrLodAACO4eSdLS2bbLl8+XK1aNHCqtsBAOAYzJH4lR49evhNtjQMQwUFBSouLtb8+fMtDQ4AAAS3WicSAwcO9EskGjRooNatW+uSSy5Rp06dLA3OrKt7jgl0CEBQqqg8FugQgN8lJ8+RqHUiMW3aNBvCAADAuZzc2qj1ZMuQkBAVFRVVGf/xxx8VEhJiSVAAAKB+qHVFwjjOtFGPx6OwsLBTDggAAKcJ0gUXlqhxIjF37lxJksvl0lNPPaUmTZr4zlVWVmrDhg1BM0cCAIBg4uTWRo0TiTlz5kj6pSKRk5Pj18YICwtTu3btlJOTY32EAAAgaNU4kdi9e7ckqW/fvlq5cqWaN29uW1AAADgJqzZ+5d1337UjDgAAHMsb6ABsVOtVG4MHD9bMmTOrjM+aNUvXXnutJUEBAID6odaJxIYNG3TllVdWGb/iiiu0YcMGS4ICAMBJDLksOYJRrVsbhw8frnaZZ2hoqEpLSy0JCgAAJ/E6eP1nrSsS3bp107Jly6qMv/TSS+rSpYslQQEA4CReuSw5glGtKxKTJ0/WoEGDtHPnTl166aWSpNzcXC1dulTLly+3PEAAABC8ap1IDBgwQKtWrdKDDz6o5cuXKzw8XN27d9c777zDa8QBAKhGsM5vsEKtEwlJ6t+/v/r37y9JKi0t1YsvvqiJEycqPz9flZWVlgYIAEB9x/LPamzYsEHp6emKj4/X7Nmzdemll2rjxo1WxgYAAIJcrSoSBQUFevbZZ/X000+rtLRU1113nTwej1atWsVESwAAjsPJrY0aVyQGDBigjh076tNPP1V2dra+//57PfbYY3bGBgCAI3gtOoJRjSsS//rXvzR27Fjddttt6tChg50xAQCAeqLGFYkPPvhAP/30kxITE5WUlKTHH39cJSUldsYGAIAjOLkiUeNEonfv3nryySf1ww8/6JZbbtFLL72k+Ph4eb1evfXWW/rpp5/sjBMAgHrLyVtk13rVRkREhIYNG6YPPvhAn332me644w7NmDFD0dHRuuqqq+yIEQAABCnTyz8lqWPHjpo1a5a+++47vfjii1bFBACAo3hd1hzByNSGVL8VEhKitLQ0paWlWXE7AAAcJVjfk2EFSxIJAABwfA5++eeptTYAAMDvG4kEAAA2C4blnzNmzJDL5dL48eN9Y0ePHtWoUaPUsmVLNWnSRIMHD1ZhYWGt7ksiAQCAzbwulyWHWVu2bNETTzyhs88+2298woQJev311/XKK69o/fr1+v777zVo0KBa3ZtEAgAABzt8+LBuvPFGPfnkk2revLlv/NChQ3r66af1yCOP6NJLL1ViYqIWLVqkDz/8sFYv4SSRAADAZoZFhxmjRo1S//79lZKS4jeen5+viooKv/FOnTqpbdu2ysvLq/H9WbUBAIDNrNre2uPxyOPx+I253W653e5qr3/ppZe0detWbdmypcq5goIChYWFqVmzZn7jMTExKigoqHFMVCQAAKgnsrKyFBUV5XdkZWVVe+3evXs1btw4vfDCC2rUqJFtMVGRAADAZlbtSpmZmamMjAy/seNVI/Lz81VUVKSePXv6xiorK7VhwwY9/vjjWrduncrLy3Xw4EG/qkRhYaFiY2NrHBOJBAAANrNqZ8sTtTF+67LLLtNnn33mN3bTTTepU6dOuvvuu9WmTRuFhoYqNzdXgwcPliRt375de/bsUXJyco1jIpEAAMCBmjZtqrPOOstvLCIiQi1btvSNDx8+XBkZGWrRooUiIyM1ZswYJScnq3fv3jV+DokEAAA2C9YtsufMmaMGDRpo8ODB8ng8Sk1N1fz582t1D5dhGMH6/Uz7c9v+gQ4BCEprC7YFOgQg6Bwr32f7M5acNsSS+/x93/OW3MdKVCQAALCZVcs/gxHLPwEAgGlUJAAAsJnj5hD8CokEAAA2s2ofiWBEawMAAJhGRQIAAJs5ebIliQQAADZzciJBawMAAJhGRQIAAJsZDp5sSSIBAIDNaG0AAABUg4oEAAA2c3JFgkQCAACbsbMlAAAwjZ0tAQAAqkFFAgAAmzFHAgAAmObkRILWBgAAMI2KBAAANmPVBgAAMI1VGwAAANWgIgEAgM2cPNmSRAIAAJs5eY4ErQ0AAGAaFQkAAGzmdXBNgkQCAACbMUcCAACY5tx6BHMkAADAKaAiAQCAzWhtAAAA09jZEgAAoBpUJAAAsBnLPwEAgGnOTSNobQAAgFNARQIAAJuxagMAAJjm5DkStDYAAIBpVCQAALCZc+sRJBIAANiOORIAAMA05kgAAABUg4oEAAA2c249gkQCAADbOXmOBK0NAABgGhUJAABsZji4uUEiAQCAzWhtAAAAVIOKBAAANnPyPhIkEgAA2My5aQStDQAAcApIJHDKup7XVVOemaLFW5ZozZ431PtPvf3ON2vVTONnT9DiLUu0fPsK3bvkPsW3iw9QtEBg3XZrur7+aqMOl+7Uhx+8rnN7nRPokFAHvDIsOYIRiQROWaPGjbTr893KuWdBtefvefIexbaN1QPD79e4K8aqaF+RHlg6Xe5wdx1HCgTWtddepYcfmqr7H3hE5yb10yeffq4333hBrVu3DHRosJnXoiMYkUjglOW/l6/nH35OeevyqpyLT4hXp8TOmv/Pedrx6Q7t27VP8/8xT2GNwtRnYJ8ARAsEzoRxI/TU00u1eMnL+uKLHbp91CQdOfKzbhp6Q6BDg80Mi/4XjEgkYKvQsFBJUrmn3DdmGIYqyivU5dyugQoLqHOhoaHq2fNs5b7zvm/MMAzlvvOBevdODGBkwKkJ6kRi7969GjZs2Amv8Xg8Ki0t9Tsqjco6ihAn893O71T0XZHS7x6qiKgmahjaUINvu0at41urRXTzQIcH1JlWrVqoYcOGKios8RsvKipWbEzrAEWFukJrI0D279+vxYsXn/CarKwsRUVF+R07S3fWUYQ4mcpjlZp+y3SdlnCaln22TCu2r9TZyWfro3e2yOsNzjIdAFjNya2NgO4jsXr16hOe37Vr10nvkZmZqYyMDL+x67ted0pxwVo7P/taY68Yo8ZNG6thaEOV7i/V7Nce0Y5PdwQ6NKDOlJTs17FjxxQd08pvPDq6tQoKiwMUFXDqAppIpKWlyeVyyTCOn2W5XK4T3sPtdsvt9p/9H+IKsSQ+WOvIT0ckSfHt4tX+7PZ6/uHnAhwRUHcqKiq0deunurTvhVq9ep2kX/77dmnfCzV/waIARwe7BWtbwgoBTSTi4uI0f/58DRw4sNrz27ZtU2Iik5CCXaPGjRT3q30hYtrEKqHLH3T44E8q/r5YF/S/UKU/HlLR98Vq17GdRk4bqY3rNurj9z8OYNRA3Zvz6JNa9PQc5W/9VFu2fKyxY0YoIiJczy5eFujQYDPvCf4Pc30X0EQiMTFR+fn5x00kTlatQHDocHYHZb08w/fziKkjJElvv/K2su+YoxbRzXXz5JvVrFUzHSg6oHdW5OqluS8FKlwgYF55ZbVat2qhaVMmKja2tT755L/q/+chKioqOfmHgSDlMgL4L/X777+vsrIy9evXr9rzZWVl+uijj9SnT+32G/hz2/5WhAc4ztqCbYEOAQg6x8r32f6MIWcMsuQ+z3+70pL7WCmgFYmLLrrohOcjIiJqnUQAABBsgnV7aysE9fJPAAAQ3HiNOAAANgvWPSCsQCIBAIDNWP4JAABMY44EAACoV7KysnTuueeqadOmio6OVlpamrZv3+53zdGjRzVq1Ci1bNlSTZo00eDBg1VYWFir55BIAABgs0C8a2P9+vUaNWqUNm7cqLfeeksVFRX605/+pLKyMt81EyZM0Ouvv65XXnlF69ev1/fff69Bg2q3VDWg+0jYhX0kgOqxjwRQVV3sIzHojKssuc/Kb0/8jqoTKS4uVnR0tNavX6+LL75Yhw4dUuvWrbV06VJdc801kqQvv/xSnTt3Vl5ennr37l2j+1KRAADgd+DQoUOSpBYtWkiS8vPzVVFRoZSUFN81nTp1Utu2bZWXl1fj+zLZEgAAm1lV/Pd4PPJ4PH5j1b288re8Xq/Gjx+vCy64QGeddZYkqaCgQGFhYWrWrJnftTExMSooKKhxTFQkAACwmVeGJUdWVpaioqL8jqysrJM+f9SoUfrPf/6jl16y/j1HVCQAAKgnMjMzlZGR4Td2smrE6NGjtWbNGm3YsEGnn366bzw2Nlbl5eU6ePCgX1WisLBQsbGxNY6JigQAADbzWnS43W5FRkb6HcdLJAzD0OjRo/Xqq6/qnXfeUUJCgt/5xMREhYaGKjc31ze2fft27dmzR8nJyTX+blQkAACwWSC2yB41apSWLl2q1157TU2bNvXNe4iKilJ4eLiioqI0fPhwZWRkqEWLFoqMjNSYMWOUnJxc4xUbEokEAACOtGDBAknSJZdc4je+aNEiDR06VJI0Z84cNWjQQIMHD5bH41Fqaqrmz59fq+eQSAAAYLNAbJFdk5UijRo10rx58zRv3jzTzyGRAADAZg7c+9GHRAIAAJs5+e2frNoAAACmUZEAAMBmgVi1UVdIJAAAsFkgJlvWFVobAADANCoSAADYjFUbAADANFobAAAA1aAiAQCAzVi1AQAATPM6eI4ErQ0AAGAaFQkAAGzm3HoEiQQAALZz8qoNEgkAAGzm5ESCORIAAMA0KhIAANiMnS0BAIBptDYAAACqQUUCAACbsbMlAAAwzclzJGhtAAAA06hIAABgMydPtiSRAADAZrQ2AAAAqkFFAgAAm9HaAAAAprH8EwAAmOZljgQAAEBVVCQAALAZrQ0AAGAarQ0AAIBqUJEAAMBmtDYAAIBptDYAAACqQUUCAACb0doAAACm0doAAACoBhUJAABsRmsDAACYZhjeQIdgGxIJAABs5uTXiDNHAgAAmEZFAgAAmxkOXrVBIgEAgM1obQAAAFSDigQAADajtQEAAExjZ0sAAIBqUJEAAMBm7GwJAABMc/IcCVobAADANCoSAADYzMn7SJBIAABgMye3NkgkAACwGcs/AQAAqkFFAgAAm9HaAAAApjl5siWtDQAAYBoVCQAAbEZrAwAAmMaqDQAAgGpQkQAAwGa8tAsAAJhGawMAAKAaVCQAALAZqzYAAIBpTp4jQWsDAACbGYZhyWHGvHnz1K5dOzVq1EhJSUnavHmzpd+NRAIAAIdatmyZMjIyNHXqVG3dulXdu3dXamqqioqKLHsGiQQAADYLVEXikUce0YgRI3TTTTepS5cuysnJUePGjfXMM89Y9t1IJAAAsJlh0VEb5eXlys/PV0pKim+sQYMGSklJUV5e3il9n19jsiUAAPWEx+ORx+PxG3O73XK73VWuLSkpUWVlpWJiYvzGY2Ji9OWXX1oWkyMTiTV73gh0CNAvf+CzsrKUmZlZ7R9y4PeKvxu/P8fK91lyn2nTpunee+/1G5s6daqmTZtmyf3NcBlOXtyKgCotLVVUVJQOHTqkyMjIQIcDBA3+bsCs2lQkysvL1bhxYy1fvlxpaWm+8fT0dB08eFCvvfaaJTExRwIAgHrC7XYrMjLS7zheVSssLEyJiYnKzc31jXm9XuXm5io5OdmymBzZ2gAAAFJGRobS09PVq1cvnXfeecrOzlZZWZluuukmy55BIgEAgENdf/31Ki4u1pQpU1RQUKBzzjlHa9eurTIB81SQSMA2brdbU6dOZTIZ8Bv83UBdGj16tEaPHm3b/ZlsCQAATGOyJQAAMI1EAgAAmEYiAQAATCORAAAAppFIwDbz5s1Tu3bt1KhRIyUlJWnz5s2BDgkIqA0bNmjAgAGKj4+Xy+XSqlWrAh0ScMpIJGCLZcuWKSMjQ1OnTtXWrVvVvXt3paamqqioKNChAQFTVlam7t27a968eYEOBbAMyz9hi6SkJJ177rl6/PHHJf2yLWubNm00ZswYTZo0KcDRAYHncrn06quv+r0DAaiPqEjAcuXl5crPz1dKSopvrEGDBkpJSVFeXl4AIwMAWI1EApYrKSlRZWVllS1YY2JiVFBQEKCoAAB2IJEAAACmkUjAcq1atVJISIgKCwv9xgsLCxUbGxugqAAAdiCRgOXCwsKUmJio3Nxc35jX61Vubq6Sk5MDGBkAwGq8/RO2yMjIUHp6unr16qXzzjtP2dnZKisr00033RTo0ICAOXz4sL7++mvfz7t379a2bdvUokULtW3bNoCRAeax/BO2efzxx/XQQw+poKBA55xzjubOnaukpKRAhwUEzHvvvae+fftWGU9PT9ezzz5b9wEBFiCRAAAApjFHAgAAmEYiAQAATCORAAAAppFIAAAA00gkAACAaSQSAADANBIJAABgGokE4EBDhw5VWlqa7+dLLrlE48ePr/M43nvvPblcLh08eLDOnw2gbpBIAHVo6NChcrlccrlcCgsLU/v27XXffffp2LFjtj535cqVuv/++2t0Lf/4A6gN3rUB1LF+/fpp0aJF8ng8evPNNzVq1CiFhoYqMzPT77ry8nKFhYVZ8swWLVpYch8A+C0qEkAdc7vdio2N1RlnnKHbbrtNKSkpWr16ta8dMX36dMXHx6tjx46SpL179+q6665Ts2bN1KJFCw0cOFDffPON736VlZXKyMhQs2bN1LJlS91111367c73v21teDwe3X333WrTpo3cbrfat2+vp59+Wt98843vXRDNmzeXy+XS0KFDJf3yBtesrCwlJCQoPDxc3bt31/Lly/2e8+abb+rMM89UeHi4+vbt6xcnAGcikQACLDw8XOXl5ZKk3Nxcbd++XW+99ZbWrFmjiooKpaamqmnTpnr//ff173//W02aNFG/fv18n5k9e7aeffZZPfPMM/rggw+0f/9+vfrqqyd85t///ne9+OKLmjt3rr744gs98cQTatKkidq0aaMVK1ZIkrZv364ffvhBjz76qCQpKytLS5YsUU5Ojv773/9qwoQJGjJkiNavXy/pl4Rn0KBBGjBggLZt26abb75ZkyZNsuu3DUCwMADUmfT0dGPgwIGGYRiG1+s13nrrLcPtdhsTJ0400tPTjZiYGMPj8fiuf+6554yOHTsaXq/XN+bxeIzw8HBj3bp1hmEYRlxcnDFr1izf+YqKCuP000/3PccwDKNPnz7GuHHjDMMwjO3btxuSjLfeeqvaGN99911DknHgwAHf2NGjR43GjRsbH374od+1w4cPN/7yl78YhmEYmZmZRpcuXfzO33333VXuBcBZmCMB1LE1a9aoSZMmqqiokNfr1V//+ldNmzZNo0aNUrdu3fzmRXzyySf6+uuv1bRpU797HD16VDt37tShQ4f0ww8/+L2evWHDhurVq1eV9sb/bNu2TSEhIerTp0+NY/7666915MgRXX755X7j5eXl6tGjhyTpiy++qPKa+OTk5Bo/A0D9RCIB1LG+fftqwYIFCgsLU3x8vBo2/L+/hhEREX7XHj58WImJiXrhhReq3Kd169amnh8eHl7rzxw+fFiS9MYbb+i0007zO+d2u03FAcAZSCSAOhYREaH27dvX6NqePXtq2bJlio6OVmRkZLXXxMXFadOmTbr44oslSceOHVN+fr569uxZ7fXdunWT1+vV+vXrlZKSUuX8/yoilZWVvrEuXbrI7XZrz549x61kdO7cWatXr/Yb27hx48m/JIB6jcmWQBC78cYb1apVKw0cOFDvv/++du/erffee09jx47Vd999J0kaN26cZsyYoVWrVunLL7/U7bfffsI9INq1a6f09HQNGzZMq1at8t3z5ZdfliSdccYZcrlcWrNmjYqLi3X48GE1bdpUEydO1IQJE7R48WLt3LlTW7du1WOPPabFixdLkm699Vbt2LFDd955p7Zv366lS5fq2Weftfu3CECAkUgAQaxx48basGGD2rZtq0GDBqlz584aPny4jh496qtQ3HHHHfrb3/6m9PR0JScnq2nTprr66qtPeN8FCxbommuu0e23365OnTppxIgRKisrkySddtppuvfeezVp0iTFxMRo9OjRkqT7779fkydPVlZWljp37qx+/frpjTfeUEJCgiSpbdu2WrFihVatWqXu3bsrJydHDz74oI2/OwCCgcs43owsAACAk6AiAQAATCORAAAAppFIAAAA00gkAACAaSQSAADANBIJAABgGokEAAAwjUQCAACYRiIBAABMI5EAAACmkUgAAADTSCQAAIBp/w/Xr09Xnsrx0QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "def plot_confusion_matrix(Y_T, X,title_name):\n",
    "    cm = confusion_matrix(Y_T, X)\n",
    "    sns.heatmap(cm, annot=True, fmt='d')\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('Actual')\n",
    "    plt.title(title_name)\n",
    "    plt.show()\n",
    "    \n",
    "plot_confusion_matrix(val_male_tag, male_rslt_af_th,\"male\")\n",
    "plot_confusion_matrix(val_female_tag, female_rslt_af_th,\"female\")\n",
    "plot_confusion_matrix(val_tag, all_rslt_af_th,\"All\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c60f675",
   "metadata": {},
   "source": [
    "# 预测输出"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6839047",
   "metadata": {},
   "source": [
    "提交，对df_norm_test进行预测，将passager_id和Survived写入sub.csv文件，"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "1725c154",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测结果已保存到 svm-672-th0.7.csv 文件\n",
      "预测样本数量: 418\n",
      "前5行预测结果:\n",
      "   PassengerId  Survived\n",
      "0          892         0\n",
      "1          893         1\n",
      "2          894         0\n",
      "3          895         0\n",
      "4          896         1\n"
     ]
    }
   ],
   "source": [
    "y_pred_test = svm_y_pred.copy()\n",
    "test_data_with_id = pd.read_csv('data/test.csv', usecols=['PassengerId'])\n",
    "# 创建提交结果DataFrame\n",
    "submission = pd.DataFrame({\n",
    "    'PassengerId': test_data_with_id['PassengerId'],\n",
    "    'Survived': y_pred_test\n",
    "})\n",
    "\n",
    "# 保存到CSV文件\n",
    "saveName=\"svm-672-th0.7.csv\"\n",
    "submission.to_csv(saveName, index=False)\n",
    "\n",
    "print(f\"预测结果已保存到 {saveName} 文件\")\n",
    "print(f\"预测样本数量: {len(submission)}\")\n",
    "print(\"前5行预测结果:\")\n",
    "print(submission.head())"
   ]
  }
 ],
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